CN110688974A

CN110688974A - Identity recognition method and device

Info

Publication number: CN110688974A
Application number: CN201910945461.9A
Authority: CN
Inventors: 赵鹏飞
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2019-09-30
Filing date: 2019-09-30
Publication date: 2020-01-14
Anticipated expiration: 2039-09-30
Also published as: CN115862088A; CN110688974B

Abstract

One or more embodiments of the present specification provide an identity recognition method and apparatus, where the method includes: the Internet of things equipment is divided into the same equipment group by grouping the Internet of things equipment with larger association degree in advance, reference face images corresponding to the Internet of things equipment in the equipment group are stored in the same shared face library, so that face images of users acquired in real time by the Internet of things equipment are compared with the shared face library of the equipment group where the Internet of things equipment is located subsequently, and then corresponding user identity recognition results are generated according to comparison results.

Description

Identity recognition method and device

Technical Field

The document relates to the technical field of the internet of things, in particular to an identity recognition method and device.

Background

At present, with the rapid development of the internet of things technology, corresponding business services are provided for users by arranging internet of things equipment at a specified position, so that convenience is brought to daily life of people; meanwhile, along with the rapid development of the face recognition technology, the face recognition technology is applied to the Internet of things equipment, and a user finishes face brushing operation by using the Internet of things equipment, so that face recognition is performed on a face image acquired based on the face brushing operation, and corresponding control operation is executed according to a user identity recognition result. For example, for a self-service cash register or a self-service vending machine based on face recognition, a user performs face brushing operation on the self-service cash register or the self-service vending machine, and automatically executes payment operation after face recognition is passed; if the intelligent access control equipment based on the face recognition is used, the user can perform face brushing operation on the intelligent access control equipment, and after the face recognition passes, the unlocking operation is automatically executed.

Currently, in order to reduce the operation steps of a user, the user identity is confirmed only based on a face image of the user, the step that the user inputs information such as a mobile phone number to confirm the user identity is omitted, and the user experience is improved; based on the method, the face image of the user acquired by the Internet of things equipment is compared with the pre-stored face image, and whether the user has the corresponding authority is determined; however, with the increasing use of the internet of things equipment, the magnitude of the pre-stored face images is larger and larger, and if a full-scale comparison mode of the face images is adopted, the face image recognition efficiency is influenced; however, if a mode of establishing a respective face database for each piece of internet of things equipment is adopted in order to reduce the comparison magnitude of the face images, a situation that the corresponding pre-stored face images cannot be searched occurs in response to a change of the internet of things equipment used by the user, and thus the face search success rate is inevitably reduced.

Accordingly, it is desirable to provide an identity recognition method capable of improving the identity recognition efficiency and ensuring the search success rate.

Disclosure of Invention

One or more embodiments of the present disclosure provide an identity recognition method and apparatus, which can improve identity recognition efficiency by reducing face image comparison data, and ensure face search success rate by targeted face image sharing, thereby achieving both identity recognition efficiency and face search success rate.

To solve the above technical problem, one or more embodiments of the present specification are implemented as follows:

one or more embodiments of the present specification provide an identity recognition method, including:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

comparing the face image with a shared face library of the equipment group to generate a comparison result;

and identifying the identity of the user according to the comparison result.

One or more embodiments of the present specification provide an identification apparatus, including:

the face image acquisition module is used for acquiring a face image of a user by using the Internet of things equipment;

the equipment group determining module is used for determining an equipment group where the Internet of things equipment is located;

the face image comparison module is used for comparing the face image with a shared face library of the equipment group to generate a comparison result;

and the user identity identification module is used for identifying the identity of the user according to the comparison result.

a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.

One or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed by a processor, implement a method of:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.

According to the identity recognition method and device in one or more embodiments of the specification, the internet of things devices are grouped in advance, the internet of things devices with a large association degree are divided into the same device group, the reference face images corresponding to the internet of things devices in the device group are stored in the same shared face library, so that the face images of the user acquired in real time by the internet of things devices are compared with the shared face library of the device group where the internet of things devices are located, and then the corresponding user identity recognition result is generated according to the comparison result. One or more embodiments of the present disclosure enable a face image matched with an internet of things device to be quickly searched from a shared face library corresponding to the device group no matter which internet of things device in the same device group is used for the same user, so that not only can the identification efficiency be improved by reducing the face image comparison data, but also the face search success rate can be ensured by targeted face image sharing, and further, both the identification efficiency and the face search success rate can be considered.

Drawings

In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some of the embodiments described in one or more of the specification, and that other drawings can be obtained by those skilled in the art without inventive exercise.

Fig. 1 is a schematic application scenario diagram of an identity recognition system provided in one or more embodiments of the present disclosure;

fig. 2 is a first schematic flow chart of an identity recognition method according to one or more embodiments of the present disclosure;

fig. 3 is a second flowchart of an identity recognition method according to one or more embodiments of the present disclosure;

fig. 4 is a schematic diagram illustrating an implementation of a grouping process of internet of things devices in an identity recognition method provided in one or more embodiments of the present specification;

fig. 5a is a schematic flow chart of an identity recognition method provided in one or more embodiments of the present disclosure;

fig. 5b is a fourth schematic flow chart of an identity recognition method according to one or more embodiments of the present disclosure;

fig. 6 is a schematic flow chart of an identity recognition method according to one or more embodiments of the present disclosure;

fig. 7 is a schematic diagram illustrating a first module of an identification apparatus according to one or more embodiments of the present disclosure;

fig. 8 is a schematic diagram illustrating a second module of an identification apparatus according to one or more embodiments of the present disclosure;

fig. 9 is a schematic structural diagram of an identification device according to one or more embodiments of the present disclosure.

Detailed Description

In order to make the technical solutions in one or more embodiments of the present disclosure better understood, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of one or more embodiments of the present disclosure, but not all embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments described in one or more of the present specification without inventive step should be considered within the scope of protection of this document.

One or more embodiments of the present disclosure provide an identity recognition method and apparatus, and for a same user, no matter which internet of things device in a same device group is used, a face image matched with the same can be quickly searched from a shared face library corresponding to the device group, so that not only can the identity recognition efficiency be improved by reducing face image comparison data, but also a face search success rate can be ensured by targeted face image sharing, and further, both the identity recognition efficiency and the face search success rate can be considered.

Fig. 1 is a schematic view of an application scenario of an identity recognition system according to one or more embodiments of the present specification, and as shown in fig. 1, the identity recognition system includes: the system comprises the equipment of the internet of things, a server and a user, wherein the server can be a background server used for managing and controlling the equipment of the internet of things and providing a certain service for the user, and can also be a background server of a certain website (such as an online shopping website or payment application) and the like. The server can be an independent server or a server cluster consisting of a plurality of servers; this thing networking device can be connected to the thing networking and for the intelligent terminal equipment that the user provided business service, for example, brush face payment equipment, brush face shopping machine by oneself, brush face and get a cabinet, brush face and get ticket machine, or brush face entrance guard's equipment etc. wherein to thing networking device for brushing face shopping machine by oneself as an example, above-mentioned identification's specific process is:

(1) the server groups the Internet of things equipment in advance according to the equipment portrait information of the Internet of things equipment to obtain a plurality of equipment groups, establishes a shared face library for each equipment group, and stores the corresponding relation among the Internet of things equipment, the equipment groups and the shared face library;

the method comprises the steps that N pieces of Internet of things equipment are grouped in advance to obtain M equipment groups, wherein M is more than 1 and less than N, a plurality of pieces of Internet of things equipment in each equipment group correspond to the same shared face library, the similarity degree of equipment portrait information among the pieces of Internet of things equipment is used for representing the probability of being used by users in the same group, namely the similarity degree of the equipment portrait information among the pieces of Internet of things equipment is positively correlated with the user contact ratio of the pieces of Internet of things equipment, specifically, the higher the similarity degree of the equipment portrait information among the pieces of Internet of things equipment is, the greater the user contact ratio of the pieces of Internet of things equipment is, and correspondingly, the higher the correlation degree of the pieces of Internet of things equipment is; that is, a plurality of internet of things devices providing service for the same user group are divided into the same device group; by predicting the probability that the same user group uses different Internet of things devices, the Internet of things devices are grouped more accurately, so that the face search range is accurately defined in a certain range in the user identity recognition process;

(2) the method comprises the steps that the Internet of things equipment collects face images of users and sends identity identification requests to a server, wherein the identity identification requests carry the collected face images of the users and equipment identification information, the users enjoy certain business services by utilizing the Internet of things equipment, and the identity identification requests can be face identification requests;

(3) after receiving the identity identification request, the server determines an equipment group where the Internet of things equipment is located according to equipment identification information carried in the identity identification request and a pre-stored corresponding relationship between the equipment and the equipment group;

(4) the server compares the face image of the user with each face image in a shared face library of an equipment group where the Internet of things equipment is located to generate a corresponding comparison result;

(5) the server generates an identity recognition result of the user according to the generated comparison result; wherein, the identification result comprises: the user identity identification is passed or fails;

in the user identity recognition process, the Internet of things equipment is grouped in advance, the plurality of Internet of things equipment with large association degree are divided into the same equipment group, the plurality of Internet of things equipment in the same equipment group use the same shared face library, the shared face library comprises reference face images of users facing all the Internet of things equipment in the equipment group, and after a follow-up server obtains face images of the users collected by certain Internet of things equipment, the identity recognition is carried out on the users on the basis of the face images in the shared face library of the equipment group where the Internet of things equipment is located. Therefore, for the same user, no matter which internet of things equipment in the same equipment group is used, the face image matched with the equipment group can be quickly searched from the shared face library corresponding to the equipment group, so that the identification efficiency can be improved by reducing the face image comparison data, the face search success rate can be ensured by targeted face image sharing, and the identification efficiency and the face search success rate are considered at the same time.

Fig. 2 is a first flowchart of an identity identification method provided in one or more embodiments of the present specification, where the method in fig. 2 can be executed by the server in fig. 1, where an execution subject is taken as an example for description, and for a case of an internet of things device, processing may be performed according to the following related content, which is not described herein again. As shown in fig. 2, the method comprises at least the following steps:

s202, collecting a face image of a user by using the Internet of things equipment; the user is a user who enjoys a certain service by using an internet of things device, and the internet of things device is an intelligent terminal device that is connected to the internet of things and provides a service for the user, for example, the internet of things device may include: any one of a face-brushing payment device, a self-service face-brushing shopping machine, a face-brushing pick-up cabinet, a face-brushing ticket-picking machine and a face-brushing access control device;

wherein, if thing networking device includes: the service can be a payment service provided based on face recognition; if the internet of things equipment comprises: the service can be an equipment unlocking service provided based on face recognition; if the Internet of things equipment is a face-brushing ticket-taking machine, correspondingly, the control operation is automatic ticket printing operation;

specifically, the internet of things equipment acquires a face image of a user through a camera device, and sends an identity recognition request to a server after the face image is acquired, wherein the identity recognition request carries the face image of the user and equipment identification information; correspondingly, the server receives the identity recognition request to acquire a face image of the user and equipment identification information of the Internet of things equipment;

s204, determining an equipment group where the Internet of things equipment is located; in specific implementation, in a process of equipment grouping, the equipment groups may be divided according to equipment portrait information of the internet of things equipment, wherein the comprehensive similarity degree of the equipment portrait information of the internet of things equipment in each equipment group is greater than a preset threshold, specifically, since the similarity degree of the equipment portrait information among the internet of things equipment is positively correlated with the user contact ratio of the internet of things equipment, the higher the similarity degree of the equipment portrait information among the internet of things equipment is, the greater the user contact ratio of the internet of things equipment is, and correspondingly, the higher the correlation degree of the internet of things equipment is;

specifically, the internet of things equipment is grouped in advance, a plurality of pieces of internet of things equipment with large association degree are divided into the same equipment group, and a shared face library is respectively constructed for each equipment group, namely a plurality of pieces of internet of things equipment in the same equipment group use the same shared face library, the shared face library comprises face images of users facing all the internet of things equipment in the equipment group, and a subsequent server firstly determines an equipment group where the internet of things equipment is located on the basis of a pre-stored corresponding relationship between the equipment and the equipment group after acquiring the face images of the users collected by the certain internet of things equipment;

s206, comparing the acquired face image with the shared face library of the determined equipment group to generate a comparison result;

specifically, the comparison result includes: similarity between the face image of the user and each face image in the shared face library, or whether the face image of the user is consistent with each face image in the shared face library or not, or whether a face image consistent with the face image of the user exists in each face image in the shared face library or not;

s208, identifying the identity of the user according to the comparison result; wherein, the identification result of the user comprises: passing or failing identity recognition;

specifically, if it is determined that at least one reference face image matched with the face image of the user exists in the shared face library based on the comparison result, it is determined that the identity recognition for the user passes; otherwise, determining that the identity recognition aiming at the user fails; wherein, the reference face image matched with the face image of the user comprises: a reference face image with the similarity degree with the face image of the user being greater than a first similarity threshold value or a reference face image consistent with the face image of the user;

specifically, in the process of user identity recognition, an equipment group where the internet of things equipment requesting identity recognition is located is determined, and then the user is subjected to identity recognition based on a face image in a shared face library corresponding to the equipment group, so that a corresponding user identity recognition result is obtained.

In one or more embodiments of the present specification, internet of things devices are grouped in advance, the internet of things devices with a relatively high association degree are divided into the same device group, reference face images corresponding to the internet of things devices in the device group are stored in the same shared face library, so that a face image of a user acquired in real time by using the internet of things devices is subsequently compared with the shared face library of the device group in which the internet of things devices are located, and then a corresponding user identification result is generated according to a comparison result. Therefore, for the same user, no matter which internet of things equipment in the same equipment group is used, the face image matched with the equipment group can be quickly searched from the shared face library corresponding to the equipment group, so that the identification efficiency can be improved by reducing the face image comparison data, the face search success rate can be ensured by targeted face image sharing, and the identification efficiency and the face search success rate are considered at the same time.

Wherein, in order to compromise identity recognition efficiency and face search success rate simultaneously, ensure to improve face search success rate under the condition of the quantity is compared to the people 'S face of minimizing, organize into groups thing networking device in advance, divide a plurality of thing networking devices that the degree of association is great into same equipment group, and a plurality of thing networking devices in same equipment group use same sharing face storehouse, based on this, as shown in fig. 3, at S202, utilize thing networking device, before gathering user' S face image, still include:

s210, grouping a plurality of Internet of things devices according to a preset device grouping mode to obtain a plurality of device groups; the comprehensive similarity degree of the equipment portrait information of the Internet of things equipment in each equipment group is larger than a preset threshold value;

wherein, the preset equipment grouping mode comprises: the method comprises the following steps that a manually calibrated equipment grouping mode or an automatic intelligent equipment grouping mode is selected according to an actual application scene during specific implementation; for example, for the case that the number of the internet of things devices is greater than the preset number threshold, preferably, a plurality of internet of things devices are grouped in an automatic intelligent device grouping manner;

specifically, no matter the equipment grouping mode of manual calibration or the automatic intelligent equipment grouping mode, the equipment portrait information is used as a grouping basis, the multiple pieces of internet of things equipment with high similarity degree of the equipment portrait information are divided into the same equipment group, and the similarity degree of the equipment portrait information among the pieces of internet of things equipment is positively correlated with the user contact ratio of the pieces of internet of things equipment, namely, the higher the similarity degree of the equipment portrait information among the pieces of internet of things equipment is, the greater the user contact ratio of the pieces of internet of things equipment is, and correspondingly, the higher the correlation degree of the pieces of internet of things equipment is;

s212, storing the corresponding relation between each Internet of things device and the device group; specifically, the corresponding relation between the equipment and the equipment group is stored so as to be used as a basis for determining the equipment group where the equipment of the internet of things is located in the following process;

s214, aiming at each equipment group, constructing a shared face library corresponding to the equipment group; specifically, the corresponding relationship between the device group and the shared face library is stored again;

correspondingly, the step S204 of determining the equipment group where the internet of things equipment is located includes:

and S2041, determining the equipment group where the Internet of things equipment is located according to the pre-stored correspondence between the equipment and the equipment group.

In step S214, for each device group, a shared face library corresponding to the device group is constructed, which specifically includes:

step one, aiming at each equipment group, acquiring a reference face image of a user facing the equipment group;

and secondly, constructing a shared face library corresponding to the equipment group based on the acquired reference face image.

Specifically, the identification information of each internet of things device contained in each device group is recorded; establishing a shared face library aiming at each equipment group, storing reference face images of users respectively facing each Internet of things equipment in the equipment group into the shared face library, and recording the corresponding relation between the identification information of each equipment group and the identification information of the shared face library so as to take the reference face images prestored in the shared face library as comparison bases of the face images of the users collected in real time in the subsequent user identity recognition process; the face images stored in the shared face library can be face feature data used for identifying the uniqueness of the user identity, and the face images in the shared face library are subjected to de-duplication processing;

specifically, the reference face image in the shared face library of the device group may be determined according to user information in a historical usage record of each internet of things device, and/or determined according to user information predicted to use each internet of things device by using a device usage prediction model, where the device usage prediction model may be obtained by performing model parameter training on a preset machine learning model based on a training sample set; that is, the reference face images in the shared face library include: and at least one of the face image of the user who uses any one of the equipment groups of the internet of things included in the equipment group corresponding to the shared face library and the face image of the user who will use any one of the equipment groups of the internet of things included in the equipment group corresponding to the shared face library within a future preset time period.

In a specific embodiment, as shown in fig. 4, if the similarity of the device image information between the internet-of-things devices with device identifiers 0001, 0025, 0048 and 0076 is higher according to the preset device grouping manner, therefore, the internet of things devices with the device identifiers 0001, 0025, 0048 and 0076 are divided into the same device group, the grouping sequence number can be set as IOT-group1, the similarity of the device image information between the internet of things devices with the device identifiers 0002, 0012, 0056, 0120 and 0325 is higher, therefore, the internet of things devices with the device identifiers of 0002, 0012, 0056, 0120, and 0325 are divided into the same device group, the grouping sequence number may be set to IOT-group2, and similarly, the N internet of things devices are divided into M device groups, that is, the grouping sequence numbers are IOT-group1, IOT-group2 … and IOT-group M respectively, wherein M is more than 1 and M is less than N;

specifically, after dividing a plurality of internet of things devices into a plurality of device groups, a shared face library is also required to be established for each device group, and each shared face library has a unique number, for example, the serial numbers of the device groups are IOT-group1, IOT-group2 …, IOT-group M, respectively, and correspondingly, the serial numbers of the shared face libraries are share-data1, share-data2 …, and share-data M, respectively.

As shown in fig. 5a, in the S210, for a process of grouping the internet of things devices in an artificial calibration manner, grouping a plurality of internet of things devices according to a preset device grouping manner to obtain a plurality of device groups specifically includes:

s2101, receiving a device grouping request for a plurality of internet of things devices, where the device grouping request carries a correspondence between device identifiers and grouping sequence numbers obtained based on device image information of the internet of things devices;

specifically, related personnel perform grouping calibration on the internet of things equipment on a display interface of a management terminal, and based on the corresponding relation between equipment identification and a grouping serial number manually input by equipment portrait information of the internet of things equipment, the management terminal generates an equipment grouping request carrying the corresponding relation and sends the equipment grouping request to a server;

in specific implementation, still taking the device grouping result in fig. 4 as an example, if the similarity degree of the device image information between the internet-of-things devices with device identifiers 0001, 0025, 0048, and 0076 is higher, the device grouping request received by the server carries the corresponding relationship between the device identifiers 0001, 0025, 0048, and 0076 and the group sequence number IOT-group1, and similarly, the device grouping request also carries the corresponding relationship between other device identifiers and the group sequence number;

s2102, grouping a plurality of Internet of things devices according to the corresponding relation between the received device identification and the grouping serial number to obtain a plurality of device groups;

specifically, a plurality of internet of things devices corresponding to the same grouping sequence number are divided into the same device group to obtain M device groups, wherein the grouping sequence numbers are IOT-group1, IOT-group2 … and IOT-group M, respectively, M is greater than 1 and M is smaller than N, and the M device groups include N internet of things devices.

As shown in fig. 5b, for the process of grouping the internet of things devices in an automatic intelligent device grouping manner, in step S210, grouping the multiple internet of things devices in a preset device grouping manner to obtain multiple device groups, specifically including:

s2103, acquiring device portrait information corresponding to a plurality of Internet of things devices to be grouped, wherein the device portrait information comprises: at least one item of equipment service type information, equipment geographical position information, equipment affiliated merchant information and user attribute information;

wherein, the using the user attribute information may include: the identity, occupation, age, gender, etc. of the user;

specifically, for the case that the device portrait information includes multiple items, the similarity degrees corresponding to the various items of device portrait information may be weighted and averaged to obtain the comprehensive similarity degree of the device portrait information;

s2104, grouping the plurality of Internet of things devices according to the device portrait information of the Internet of things devices to obtain a plurality of device groups;

specifically, a plurality of pieces of internet-of-things equipment with high similarity of equipment image information are divided into the same equipment group; when equipment is grouped for the first time, clustering a plurality of existing Internet of things equipment by using a preset clustering algorithm and based on equipment portrait information of the existing Internet of things equipment to obtain a plurality of equipment cluster clusters, wherein each equipment cluster is used as an initial equipment group; determining a plurality of initial device groups as a current plurality of device groups; wherein, any one of the existing clustering algorithms can be selected to obtain a plurality of device clustering clusters, for example, K-means algorithm, DBSCAN clustering algorithm or BIRCH clustering algorithm can be selected,

next, with continuous supplement of the internet of things devices, continuously updating the initial device group, and further continuously updating the current multiple device groups, specifically, when device grouping is not performed for the first time, if a new internet of things device exists, determining the new internet of things device as the internet of things device to be grouped, and grouping the new internet of things device, where the new internet of things device may be divided into the established initial device group, and may also be divided into a newly established initial device group.

Specifically, for the process of grouping the internet of things devices by using an automatic intelligent device grouping manner and based on the device portrait information, if a plurality of initial device groups are established, that is, the device grouping is not performed for the first time, in step S2104, a plurality of internet of things devices are grouped according to the device portrait information of each internet of things device, so as to obtain a plurality of device groups, specifically, the process includes:

step one, aiming at each piece of Internet of things equipment to be marshalled, respectively determining the matching degree of the Internet of things equipment and a plurality of established initial equipment groups according to equipment portrait information of the Internet of things equipment; the higher the matching degree of the internet of things equipment and the initial equipment group is, the higher the similarity degree of the equipment portrait information of the internet of things equipment and the internet of things equipment divided into the initial equipment group is;

wherein the established plurality of initial device groups comprise: when equipment grouping is carried out for the first time, clustering a plurality of equipment clustering clusters obtained by utilizing a preset clustering algorithm based on equipment portrait information of existing Internet of things equipment, and/or grouping newly-built initial equipment when matching degrees are all smaller than a preset matching degree threshold value; subsequently, once newly-added internet-of-things equipment exists, determining the newly-added internet-of-things equipment as the internet-of-things equipment to be grouped, so that the newly-added internet-of-things equipment is grouped continuously on the basis of the established multiple initial equipment groups, and updated current multiple equipment groups are obtained;

specifically, corresponding dimension information similarity calculation is carried out on equipment portrait information of the Internet of things equipment and multi-dimension attribute information of an initial equipment group, and the matching degree of the Internet of things equipment and the initial equipment group is determined according to the weighted average value of the information similarity of all dimensions; the information dimension related to the multi-dimensional attribute information of the equipment group is the same as the information dimension related to the equipment portrait information; for example, if the device representation information includes: the device geographical position information, the device affiliated merchant information, the user attribute information, and correspondingly, the multidimensional attribute information of the initial device grouping includes: the device geographical position range, the merchant category to which the device belongs, and the user attribute information category;

determining a target equipment group matched with the Internet of things equipment to be grouped according to the determined matching degrees, and dividing the Internet of things equipment into the target equipment group;

specifically, whether at least one matching degree in the determined multiple matching degrees is larger than a preset matching degree threshold value is judged; if so, determining the initial equipment group with the maximum matching degree as a target equipment group; if not, establishing an initial equipment group according to the equipment portrait information of the Internet of things equipment to be grouped, and determining the established initial equipment group as a target equipment group; the newly-built multi-dimensional attribute information of the initial equipment group is determined according to the category of each piece of dimensional information in the equipment portrait information of the Internet of things equipment to be grouped;

determining a plurality of equipment groups based on the plurality of initial equipment groups and the target equipment groups; and determining the plurality of device groups as an established plurality of initial device groups for a next device grouping;

specifically, in the case where the target device group is any one of the plurality of initial device groups that have been established, a combination of the target device group and the other initial device groups except the initial device group having the largest degree of matching is determined as the plurality of device groups; determining a combination of a plurality of established initial equipment groups and the newly-established initial equipment group as a plurality of equipment groups aiming at the condition that the target equipment group is the newly-established initial equipment group;

when the equipment is grouped for the first time, the equipment portrait information of the internet of things equipment in each equipment cluster is similar, the multidimensional attribute information of the equipment cluster is determined according to the equipment portrait information of the internet of things equipment contained in the equipment cluster, and when the equipment is grouped for the non-first time, the internet of things equipment to be grouped is divided into target equipment groups with the matching degree larger than the preset matching degree threshold value according to the equipment portrait information, so that the obtained equipment portrait information of the internet of things equipment in each equipment group is also similar.

Wherein, to the determining process of the equipment group where the internet of things equipment is located, the above S2041 determines the equipment group where the internet of things equipment is located according to the pre-stored correspondence between the equipment and the equipment group, and specifically includes:

acquiring equipment identification information of the Internet of things equipment; specifically, the device identification information may be obtained from an identity identification request sent by the internet of things device;

determining grouping identification information corresponding to the obtained equipment identification information according to a pre-stored corresponding relation between equipment and an equipment group;

determining the equipment group with the determined grouping identification information as the equipment group where the Internet of things equipment is located; specifically, after the equipment group where the internet of things equipment is located is determined, the shared face library corresponding to the equipment group where the internet of things equipment is located can be determined;

specifically, a first corresponding relationship between identification information of each piece of internet-of-things equipment and identification information of an equipment group (namely, grouping identification information) is stored in advance, and a second corresponding relationship between identification information of each piece of equipment and identification information of a shared face library is also stored, so that the equipment group where the piece of internet-of-things equipment is located and the shared face library corresponding to the equipment group can be determined according to the first corresponding relationship, the second corresponding relationship and the identification information of the piece of internet-of-things equipment requesting identity recognition;

for example, still taking the device grouping result in fig. 4 as an example, if the identification information of the internet of things device requesting identity recognition is 0056, determining that the corresponding grouping sequence number is IOT-group2, and further determining that the corresponding shared library identification information is share-data2, so that the device group with the grouping sequence number of IOT-group2 is determined as the device group where the internet of things device requesting identity recognition is located, and the face image collected by the internet of things device is compared with the reference face image in the shared face library with the share-data 2.

In order to further improve the success rate of face search, the relevancy among the equipment groups is determined in advance according to the multidimensional attribute information of the equipment groups, so that not only is a shared face library directly corresponding to the internet of things equipment requesting user identity identification determined as a shared face library, but also an indirectly corresponding shared face library is determined as a shared face library, wherein the indirectly corresponding shared face library comprises: the shared face library directly corresponds to the equipment group with the correlation degree larger than the preset correlation degree threshold value, wherein the equipment group is located by the Internet of things equipment requesting the user identity identification, so that in the process of comparing the face images, if the shared face library directly corresponding to the equipment group cannot be searched, the shared face library indirectly corresponding to the equipment group continues to be searched;

based on this, the above-mentioned shared face storehouse of confirming that thing networking device belongs to the equipment group corresponds specifically includes:

determining a first shared face library (namely a directly corresponding shared face library) corresponding to the equipment group where the Internet of things equipment is located according to the second corresponding relation; and the number of the first and second groups,

determining at least one associated equipment group with the correlation degree of the equipment group where the Internet of things equipment is located being greater than a preset correlation degree threshold value according to the pre-stored correlation degree between the equipment groups;

determining a second shared face library (namely an indirectly corresponding shared face library) corresponding to at least one associated device group according to the second corresponding relation;

and determining the first shared face library and the second shared face library as shared face libraries corresponding to the equipment group where the Internet of things equipment is located.

Correspondingly, if the number of the shared face libraries corresponding to the equipment group where the internet of things equipment is located is determined to be multiple, the shared face libraries include: based on the first shared face library and the at least one second shared face library, in the comparison process of the face images, in step S206, the acquired face images are compared with the shared face libraries of the determined device group, so as to generate a comparison result, which specifically includes:

comparing the collected face images with all reference face images in a first shared face library to generate a first comparison result;

if the first comparison result represents that the face search is successful, stopping the face image comparison, and continuing to execute the step S208;

if the first comparison result represents that the face search fails, sequentially selecting one second shared face library from at least one second shared face library according to the sequence of the correlation degrees from large to small, and comparing the acquired face image with each reference face image in the selected second shared face library to generate a second comparison result;

if the second comparison result represents that the face search is successful, stopping the face image comparison, and continuing to execute the step S208;

and if the second comparison result represents that the face search fails, selecting a next second shared face library from at least one second shared face library until the currently selected second shared face library is the last second shared face library.

Based on that, as shown in fig. 6, after the step S208 of identifying the identity of the user according to the comparison result, the method further includes:

s214, judging whether the identity recognition result of the user passes the recognition;

if the determination result is yes, S216 triggers to execute a corresponding control operation, where the control operation includes: any one of payment operation, unlocking operation and bill printing operation;

specifically, the type of the control operation is determined according to the type of the internet of things device, for example, if the internet of things device is a face-brushing payment device or a self-service face-brushing shopping machine, the control operation is an automatic payment operation; if the user swipes the face to take the part cabinet or swipes the face to access the equipment, correspondingly, controlling the automatic unlocking operation of the operation; if the Internet of things equipment is a face-brushing ticket-taking machine, correspondingly, the control operation is automatic ticket printing operation;

if the judgment result is no, S218, carrying out identity recognition failure prompt on the user;

specifically, when the face image matched with the face image of the user does not exist in the shared face library, the identity recognition failure for the user is determined, and corresponding prompt information is displayed to the user, so that the user can check the reason of the identity recognition failure and can apply for identity recognition again.

In the identity recognition method in one or more embodiments of the present specification, the internet of things devices are grouped in advance, the internet of things devices with a relatively high association degree are divided into the same device group, the reference face images corresponding to the internet of things devices in the device group are stored in the same shared face library, so that the face image of the user acquired in real time by using the internet of things devices is compared with the shared face library of the device group in which the internet of things devices are located, and then a corresponding user identity recognition result is generated according to the comparison result. One or more embodiments of the present disclosure enable a face image matched with an internet of things device to be quickly searched from a shared face library corresponding to the device group no matter which internet of things device in the same device group is used for the same user, so that not only can the identification efficiency be improved by reducing the face image comparison data, but also the face search success rate can be ensured by targeted face image sharing, and further, both the identification efficiency and the face search success rate can be considered.

Corresponding to the identity recognition methods described in fig. 2 to fig. 6, based on the same technical concept, one or more embodiments of the present specification further provide an identity recognition apparatus, and fig. 7 is a schematic diagram of a first module of the identity recognition apparatus provided in one or more embodiments of the present specification, where the apparatus is configured to perform the identity recognition methods described in fig. 2 to fig. 6, and as shown in fig. 7, the apparatus includes:

the face image acquisition module 701 is used for acquiring a face image of a user by using the internet of things equipment;

a device group determining module 702, configured to determine a device group in which the internet of things device is located;

a face image comparison module 703, configured to compare the face image with a shared face library of the device group, and generate a comparison result;

and the user identity identification module 704 is configured to identify the identity of the user according to the comparison result.

Optionally, as shown in fig. 8, the apparatus further includes: a device grouping module 705 and a shared library construction module 706;

the device grouping module 705 is configured to group a plurality of internet of things devices according to a preset device grouping manner to obtain a plurality of device groups; and storing the corresponding relation between each Internet of things device and the device group; the comprehensive similarity degree of the equipment portrait information of the Internet of things equipment in each equipment group is larger than a preset threshold value;

the shared library construction module 706 is configured to construct, for each device group, a shared face library corresponding to the device group;

correspondingly, the device group determining module 702 is specifically configured to:

and determining the equipment group where the Internet of things equipment is located according to the corresponding relation.

Optionally, the shared library constructing module 706 is specifically configured to:

aiming at each equipment group, acquiring a reference face image of a user facing the equipment group;

and constructing a shared face library corresponding to the equipment group based on the acquired reference face image.

Optionally, the internet of things device includes: any one of face-brushing payment equipment, a self-service face-brushing shopping machine, a face-brushing pick-up cabinet, a face-brushing ticket-picking machine and face-brushing access control equipment.

Optionally, the device grouping module 705 is specifically configured to:

receiving equipment grouping requests for a plurality of pieces of Internet of things equipment, wherein the equipment grouping requests carry corresponding relations between equipment identifications and grouping serial numbers obtained based on equipment image information of the pieces of Internet of things equipment;

and grouping the plurality of Internet of things devices according to the corresponding relationship between the device identification and the grouping sequence number to obtain a plurality of device groups.

Optionally, the device grouping module 705 is further specifically configured to:

acquiring device portrait information corresponding to a plurality of pieces of internet-of-things devices to be grouped, wherein the device portrait information comprises: at least one item of equipment service type information, equipment geographical position information, equipment affiliated merchant information and user attribute information;

and grouping the plurality of Internet of things devices according to the device portrait information of each Internet of things device to obtain a plurality of device groups.

for each piece of Internet of things equipment to be grouped, respectively determining the matching degree of the piece of Internet of things equipment and the established initial equipment groups according to the equipment portrait information of the piece of Internet of things equipment;

determining a target equipment group matched with the Internet of things equipment according to the determined matching degrees, and dividing the Internet of things equipment into the target equipment groups;

determining a plurality of device groups based on the plurality of initial device groups and the target device group.

Optionally, the device group determining module 702 is further specifically configured to:

acquiring equipment identification information of the Internet of things equipment;

determining grouping identification information corresponding to the equipment identification information according to the corresponding relation;

and determining the device group with the group identification information as a device group in which the Internet of things device is located.

Optionally, the apparatus further comprises: a preset operation control module 707 for:

if the identification result of the user is that the identification is passed, executing corresponding control operation, wherein the control operation comprises the following steps: any one of payment operation, unlocking operation and bill printing operation;

and if the identification result of the user is identification failure, carrying out identification failure prompt on the user.

In the identity recognition device in one or more embodiments of the present description, the internet of things devices are grouped in advance, the internet of things devices with a relatively large association degree are divided into the same device group, the reference face images corresponding to the internet of things devices in the device group are stored in the same shared face library, so that the face images of the user acquired in real time by using the internet of things devices are compared with the shared face library of the device group in which the internet of things devices are located, and then the corresponding user identity recognition result is generated according to the comparison result. One or more embodiments of the present disclosure enable a face image matched with an internet of things device to be quickly searched from a shared face library corresponding to the device group no matter which internet of things device in the same device group is used for the same user, so that not only can the identification efficiency be improved by reducing the face image comparison data, but also the face search success rate can be ensured by targeted face image sharing, and further, both the identification efficiency and the face search success rate can be considered.

It should be noted that the embodiment of the identity recognition apparatus in this specification and the embodiment of the identity recognition method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the corresponding identity recognition method, and repeated details are not described again.

Further, on the basis of the same technical concept, corresponding to the methods shown in fig. 2 to 6, one or more embodiments of the present specification further provide an identification device, which is configured to perform the above-mentioned identification method, as shown in fig. 9.

The identification devices may vary greatly in configuration or performance and may include one or more processors 901 and memory 902, where the memory 902 may store one or more stored applications or data. Memory 902 may be, among other things, transient storage or persistent storage. The application stored in memory 902 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for an identification device. Still further, processor 901 may be configured to communicate with memory 902 to execute a series of computer-executable instructions in memory 902 on the identification device. The identification apparatus may also include one or more power supplies 903, one or more wired or wireless network interfaces 904, one or more input-output interfaces 905, one or more keyboards 906, and the like.

In one particular embodiment, an identification appliance includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the identification appliance, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.

Optionally, the computer executable instructions, when executed, further include, before acquiring a facial image of the user using the internet of things device:

grouping a plurality of Internet of things devices according to a preset device grouping mode to obtain a plurality of device groups; the comprehensive similarity degree of the equipment portrait information of the Internet of things equipment in each equipment group is larger than a preset threshold value;

storing the corresponding relation between each Internet of things device and the device group;

aiming at each equipment group, constructing a shared face library corresponding to the equipment group;

correspondingly, the determining the device group where the internet of things device is located includes:

Optionally, when executed, the computer-executable instructions, for each device group, construct a shared face library corresponding to the device group, including:

Optionally, the internet of things device comprises, when executed, computer executable instructions: any one of face-brushing payment equipment, a self-service face-brushing shopping machine, a face-brushing pick-up cabinet, a face-brushing ticket-picking machine and face-brushing access control equipment.

Optionally, when executed, the computer-executable instructions group a plurality of internet of things devices according to a preset device grouping manner to obtain a plurality of device groups, where the method includes:

Optionally, when executed, the computer-executable instructions group the multiple pieces of internet-of-things equipment according to the equipment portrait information of each piece of internet-of-things equipment, so as to obtain multiple equipment groups, where the method includes:

Optionally, when executed, the determining, according to the correspondence, a device group in which the internet of things device is located includes:

Optionally, when executed, the computer-executable instructions, after generating a user identification result for the user according to the comparison result, further include:

In the identity recognition device in one or more embodiments of the present description, the internet of things devices are grouped in advance, the internet of things devices with a relatively large association degree are divided into the same device group, the reference face images corresponding to the internet of things devices in the device group are stored in the same shared face library, so that the face images of the user acquired in real time by using the internet of things devices are subsequently compared with the shared face library of the device group in which the internet of things devices are located, and then a corresponding user identity recognition result is generated according to the comparison result. One or more embodiments of the present disclosure enable a face image matched with an internet of things device to be quickly searched from a shared face library corresponding to the device group no matter which internet of things device in the same device group is used for the same user, so that not only can the identification efficiency be improved by reducing the face image comparison data, but also the face search success rate can be ensured by targeted face image sharing, and further, both the identification efficiency and the face search success rate can be considered.

It should be noted that the embodiment of the identity recognition device in this specification and the embodiment of the identity recognition method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the corresponding identity recognition method, and repeated details are not described again.

Further, based on the same technical concept, corresponding to the methods shown in fig. 2 to fig. 6, one or more embodiments of the present specification further provide a storage medium for storing computer-executable instructions, where in a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, and the like, and the storage medium stores computer-executable instructions that, when executed by a processor, implement the following processes:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.

Optionally, the storage medium stores computer executable instructions that, when executed by the processor, further include, before acquiring a facial image of a user using the internet of things device:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, construct, for each of the device groups, a shared face library corresponding to the device group, including:

Optionally, the storage medium stores computer executable instructions that, when executed by the processor, the internet of things device comprises: any one of face-brushing payment equipment, a self-service face-brushing shopping machine, a face-brushing pick-up cabinet, a face-brushing ticket-picking machine and face-brushing access control equipment.

Optionally, when executed by the processor, the computer-executable instructions stored in the storage medium group a plurality of internet of things devices according to a preset device grouping manner to obtain a plurality of device groups, where the method includes:

Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium group the multiple internet of things devices into multiple device groups according to the device portrait information of each internet of things device, where the method includes:

Optionally, when executed by a processor, the determining, according to the correspondence, a device group in which the internet of things device is located includes:

Optionally, the computer-executable instructions stored in the storage medium, when executed by the processor, further include, after generating a user identification result for the user according to the comparison result:

When executed by a processor, the computer-executable instructions stored in the storage medium in one or more embodiments of the present specification group internet-of-things devices in advance, divide the internet-of-things devices with a relatively large association degree into a same device group, store reference face images corresponding to each internet-of-things device in the device group in a same shared face library, so as to compare face images of a user acquired in real time by using the internet-of-things devices with the shared face library of the device group in which the internet-of-things devices are located, and generate a corresponding user identification result according to a comparison result. One or more embodiments of the present disclosure enable a face image matched with an internet of things device to be quickly searched from a shared face library corresponding to the device group no matter which internet of things device in the same device group is used for the same user, so that not only can the identification efficiency be improved by reducing the face image comparison data, but also the face search success rate can be ensured by targeted face image sharing, and further, both the identification efficiency and the face search success rate can be considered.

It should be noted that the embodiment of the storage medium in this specification and the embodiment of the identity recognition method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the corresponding identity recognition method, and repeated details are not described again.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), Cal, jhdware Description Language, langua, mylar, pams, Hardware (Hardware Description Language), langva, Lola, HDL, palmware, Hardware (Hardware Description Language), VHDL (Hardware Description Language), and the like, which are currently used in the most popular languages. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations of one or more of the present descriptions.

As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied in the medium.

One or more of the present specification has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to one or more embodiments of the specification. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more of the present specification can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more of the present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is merely illustrative of one or more embodiments of the present disclosure and is not intended to limit one or more embodiments of the present disclosure. Various modifications and alterations to one or more of the present descriptions will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of one or more of the present specification should be included in the scope of one or more claims of the present specification.

Claims

1. An identity recognition method, comprising:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.

2. The method of claim 1, wherein prior to acquiring the facial image of the user with the internet of things device, further comprising:

storing the corresponding relation between each Internet of things device and the device group; and the number of the first and second groups,

3. The method of claim 2, wherein the constructing, for each of the device groups, a shared face library corresponding to the device group comprises:

4. The method of claim 2, wherein the internet of things device comprises: any one of face-brushing payment equipment, a self-service face-brushing shopping machine, a face-brushing pick-up cabinet, a face-brushing ticket-picking machine and face-brushing access control equipment.

5. The method of claim 2, wherein the grouping the multiple internet of things devices according to a preset device grouping manner to obtain multiple device groups comprises:

6. The method of claim 2, wherein the grouping the multiple internet of things devices according to a preset device grouping manner to obtain multiple device groups comprises:

7. The method of claim 6, wherein the grouping the plurality of internet of things devices according to the device portrait information of each of the internet of things devices to obtain a plurality of device groups comprises:

8. The method of claim 2, wherein the determining, according to the correspondence, the device group in which the internet of things device is located includes:

9. The method according to any one of claims 1 to 8, wherein after identifying the identity of the user according to the comparison result, further comprising:

10. An identification device comprising:

11. The apparatus of claim 10, wherein the apparatus further comprises: the device grouping module and the shared library construction module;

the equipment grouping module is used for grouping a plurality of pieces of Internet of things equipment according to a preset equipment grouping mode to obtain a plurality of equipment groups; storing the corresponding relation between each Internet of things device and the device group; the comprehensive similarity degree of the equipment portrait information of the Internet of things equipment in each equipment group is larger than a preset threshold value;

the shared library construction module is used for constructing a shared face library corresponding to each equipment group;

correspondingly, the device group determining module is specifically configured to:

12. The apparatus according to claim 11, wherein the shared library construction module is specifically configured to:

13. The apparatus of claim 11, wherein the internet of things device comprises: any one of face-brushing payment equipment, a self-service face-brushing shopping machine, a face-brushing pick-up cabinet, a face-brushing ticket-picking machine and face-brushing access control equipment.

14. The apparatus of claim 11, wherein the device grouping module is specifically configured to:

15. The apparatus of claim 11, wherein the device grouping module is further specifically configured to:

16. The apparatus of claim 15, wherein the device grouping module is further specific to:

17. The apparatus of claim 11, wherein the device group determination module is specifically configured to:

18. The apparatus of any of claims 10 to 17, wherein the apparatus further comprises: a preset operation control module for:

19. An identification device comprising:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.

20. A storage medium storing computer-executable instructions that, when executed by a processor, implement a method of:

acquiring a face image of a user by using the Internet of things equipment;

determining a device group where the Internet of things device is located;

and identifying the identity of the user according to the comparison result.