CN111538968A - Identity verification method, device and equipment based on privacy protection - Google Patents
Identity verification method, device and equipment based on privacy protection Download PDFInfo
- Publication number
- CN111538968A CN111538968A CN202010462097.3A CN202010462097A CN111538968A CN 111538968 A CN111538968 A CN 111538968A CN 202010462097 A CN202010462097 A CN 202010462097A CN 111538968 A CN111538968 A CN 111538968A
- Authority
- CN
- China
- Prior art keywords
- image
- user
- target image
- format
- identity verification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Data Mining & Analysis (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Human Computer Interaction (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioethics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Image Analysis (AREA)
Abstract
The embodiment of the specification provides an identity verification method, an identity verification device and identity verification equipment based on privacy protection, wherein the method comprises the following steps: acquiring a user image in an airspace format of a user to be checked; the user image comprises a face of a user to be verified; carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format; and according to the target image and the pre-trained face recognition model, checking the identity of the user to be checked to obtain identity checking result information.
Description
Technical Field
The present document relates to the field of identity authentication technologies, and in particular, to an identity verification method, an identity verification device, and an identity verification apparatus based on privacy protection.
Background
Face recognition technology has been developed in recent years and is applied in many scenes, such as payment, attendance, travel, and the like. In the current face recognition technology, each step of operation is performed based on the acquired original face image. Thus, once the algorithm is compromised, the user's facial image will be revealed. The leaked face image is likely to be processed by a lawbreaker and then used for attacking a face recognition system or being retrieved in a database to acquire other information (such as age, address and the like) of the user, which threatens the personal and property safety of the user.
Disclosure of Invention
One or more embodiments of the present specification provide an identity verification method based on privacy protection. The method comprises the step of obtaining a user image in a spatial domain format of a user to be checked. Wherein the user image comprises a face of the user to be verified. And carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format. And according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
One or more embodiments of the present specification provide an identity verification apparatus based on privacy protection. The device comprises an acquisition module for acquiring the user image of the airspace format of the user to be checked. Wherein the user image comprises a face of the user to be verified. The device also comprises a conversion module which carries out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format. The device also comprises a verification module, which is used for verifying the identity of the user to be verified according to the target image and the pre-trained face recognition model to obtain identity verification result information.
One or more embodiments of the present specification provide an identity verification device based on privacy protection. The apparatus includes a processor. The apparatus also comprises a memory arranged to store computer executable instructions. The computer executable instructions, when executed, cause the processor to obtain a user image in a spatial domain format of a user to be verified. Wherein the user image comprises a face of the user to be verified. And carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format. And according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
One or more embodiments of the present specification provide a storage medium. The storage medium is used to store computer-executable instructions. The computer-executable instructions, when executed, obtain a user image in a spatial domain format of a user to be verified. Wherein the user image comprises a face of the user to be verified. And carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format. And according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
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 embodiments described in the present specification, and that other drawings can be obtained by those skilled in the art without inventive exercise.
Fig. 1 is a schematic view of a scenario of an identity verification method based on privacy protection according to one or more embodiments of the present specification;
fig. 2 is a first flowchart of a privacy protection-based identity verification method according to one or more embodiments of the present disclosure;
fig. 3 is a second flowchart of an identity verification method based on privacy protection according to one or more embodiments of the present disclosure;
fig. 4 is a schematic flow chart of a privacy protection-based identity verification method according to one or more embodiments of the present disclosure;
fig. 5 is a fourth flowchart of an identity verification method based on privacy protection according to one or more embodiments of the present disclosure;
FIG. 6 is a schematic flow chart of a training method for a face recognition model according to one or more embodiments of the present disclosure;
fig. 7 is a fifth flowchart of a privacy protection-based identity verification method according to one or more embodiments of the present disclosure;
fig. 8 is a sixth flowchart of a privacy protection-based identity verification method according to one or more embodiments of the present disclosure;
fig. 9 is a schematic block diagram illustrating an identity verification apparatus based on privacy protection according to one or more embodiments of the present disclosure;
fig. 10 is a schematic structural diagram of an identity verification device based on privacy protection according to one or more embodiments of the present specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
Fig. 1 is a schematic view of an application scenario of an identity verification method based on privacy protection according to one or more embodiments of the present specification, as shown in fig. 1, the scenario includes: the system comprises an image acquisition device and an identity verification device; wherein, the image acquisition device such as a camera can be arranged in the identity verification device, and can also be separated from the identity verification device and exist independently (only the separated situation is shown in fig. 1).
Specifically, the image acquisition device acquires a user image in an airspace format of a user to be checked, wherein the user image comprises a face of the user to be checked. The identity verification device acquires a user image from the image acquisition device, and performs format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format; and according to the target image and the pre-trained face recognition model, checking the identity of the user to be checked to obtain identity checking result information. The spatial domain is also called a spatial domain (pixel domain), and the image content, such as people, plants, houses, etc., can be clearly seen from the spatial format image. The frequency domain, also called frequency domain, has its argument frequency, i.e. the horizontal axis is frequency and the vertical axis is the amplitude of the frequency signal, i.e. a spectrogram in general; therefore, the image content cannot be seen from the image in the frequency domain format. Therefore, the user image in the spatial domain format is converted into the target image in the frequency domain format, and the identity verification processing is carried out on the basis of the target image in the frequency domain format, so that even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content, the safety of the user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
It should be noted that the above application scenario is only used for illustration and is not used for limitation, the image acquisition device may also be connected to the identity verification device through another service system, and accordingly, the identity verification device may also obtain the user image in the spatial domain format of the user to be verified from the service system.
Based on the application scenario architecture, one or more embodiments of the present specification provide an identity verification method based on privacy protection. Fig. 2 is a flowchart illustrating an identity verification method based on privacy protection according to one or more embodiments of the present specification, where the method in fig. 2 can be performed by the identity verification apparatus in fig. 1, as shown in fig. 2, and the method includes the following steps:
step S102, obtaining a user image of a spatial domain format of a user to be checked; the user image comprises a face of a user to be verified;
optionally, the identity verification device is connected to the image acquisition device and receives the user image in the spatial domain format of the user to be verified, which is sent by the image acquisition device. Or the identity verification device is connected with the image acquisition device, the image acquisition device stores the acquired user image in the airspace format to a specified storage area, and the identity verification device acquires the user image in the airspace format of the user to be verified from the specified storage area. Or the identity verification device is connected with the service system and receives the user image in the airspace format of the user to be verified, which is sent by the service system. In this specification, the user image acquisition mode is not particularly limited, and may be set as needed in practical applications.
Further, the user image may be a whole body image of the user to be verified, an upper body image of the user to be verified, or a head image of the user to be verified, which is not specifically limited in this specification.
Step S104, carrying out format conversion processing on the acquired user image according to a preset mode to obtain a target image in a frequency domain format;
and step S106, checking the identity of the user to be checked according to the target image and the pre-trained face recognition model to obtain identity checking result information.
In one or more embodiments of the present description, an identity verification process is performed by converting a user image in a spatial domain format into a target image in a frequency domain format and performing identity verification processing based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
In view of that the human face of the user in the user image may be in a tilted state due to the acquisition angle, the user pose, or the like, so as to be unfavorable for the subsequent identity verification processing, in order to improve the accuracy of the identity verification processing, in one or more embodiments of the present specification, as shown in fig. 3, step S104 includes:
step S104-2, preprocessing the user image to obtain an image to be converted;
specifically, as shown in fig. 4, step S104-2 includes:
step S104-22, carrying out face detection processing on the user image according to a preset detection algorithm to obtain key position information of a face;
wherein, the key position information includes the position information of the eyes, eyebrows, nose, mouth and other parts; the key position and the detection algorithm can be set automatically according to the requirement in practical application.
And S104-24, calibrating the user image according to the obtained key position information to obtain an image to be converted.
Specifically, according to the obtained key position information and a preset calibration template, operations such as translation and turning are performed on the user image, and an image to be converted is obtained.
And step S104-4, performing format conversion processing on the image to be converted according to a preset mode to obtain a target image in a frequency domain format.
Specifically, according to a preset mode and preset transformation parameters, format conversion processing is performed on the image to be converted, and a target image in a frequency domain format is obtained. The preset manner is not specifically limited in this specification, and may be set in an actual application as needed. Since fourier transform, discrete cosine transform, and the like are well-known techniques to those skilled in the art, detailed processing procedures for fourier transform, discrete cosine transform, and the like will not be described in detail in this specification. It should be noted that the process of format conversion processing is reversible, that is, the target image is subjected to format conversion processing according to the conversion parameters, and an image to be converted in a spatial domain format (i.e., a calibrated user image) can be obtained; since the attacker cannot know the transformation parameters, the attacker cannot restore the user image even if the target image is obtained; however, for the identity verification device, the target image can be subjected to format conversion processing based on preset conversion parameters to restore the user image when needed.
Therefore, the user image is preprocessed and aligned to the calibration template, the face in the obtained image to be converted is ensured to be in a position which is beneficial to recognition, and therefore the accuracy of identity verification can be improved. By converting the format of the preprocessed image to be converted, the privacy protection of the user is realized, and the format of the target image can be converted according to the conversion parameters corresponding to the preset mode when the user needs to follow, so that the user image in the airspace format is obtained, and the effect of storing the original image is achieved.
In order to avoid the living body attack by a lawbreaker, in one or more embodiments of the present disclosure, during the identity verification process, a living body detection is performed first, and after the living body detection passes, a face comparison process is performed. Specifically, as shown in fig. 5, step S106 includes:
step S106-2, carrying out living body detection processing on the target image according to a pre-trained living body detection model;
and S106-4, if the result of the living body detection processing is that the detection is passed, carrying out face comparison processing on the target image according to a pre-trained face comparison model.
Specifically, a target image is input into a pre-trained living body detection model, living body detection processing is carried out to obtain living body detection result information, and if the living body detection result information is that detection fails, living body detection failure information is displayed; if the living body detection result information is that the detection is passed, acquiring a face image of a user to be verified from a designated database, carrying out face comparison processing on the acquired face image and a target image based on a pre-trained face comparison model to obtain face comparison result information, and determining identity verification result information according to the face comparison result information. Wherein the designated database may be an authority database, which has feasibility, such as a public security organization database; the designated database may also be a database local to the identity verification device, including valid facial images provided by each user when the user first performs the relevant service.
Therefore, before the face comparison processing, the living body detection processing is carried out at first, so that the act of malicious pretending by other people by adopting illegal means is effectively avoided, and the risks of property loss and the like of the user are further avoided.
Further, in order to implement the verification process of the identity of the user to be verified, in one or more embodiments of the present specification, as shown in fig. 6, before step S106, the method further includes:
s100-2, acquiring a sample set to be trained; wherein the sample set comprises a plurality of sample images in a spatial domain format;
s100-4, performing format conversion processing on each sample image in the sample set according to a preset mode to obtain a sample image in a frequency domain format;
specifically, each sample image in the sample set is preprocessed, and format conversion processing is performed on each preprocessed sample image according to a preset mode, so that a sample image in a frequency domain format is obtained. The specific process of the preprocessing can be referred to the related description, and repeated details are not repeated here.
And S100-6, performing model training processing based on the sample image in the frequency domain format to obtain a face recognition model.
Specifically, a sample image in a frequency domain form is divided into a training set and a test set, and the training set is trained based on a convolutional neural network to obtain an initial model; testing the obtained initial model by adopting a test set to obtain the accuracy of the initial model; determining whether the obtained accuracy is not less than a preset accuracy, if so, determining that the test processing is passed, and determining the corresponding initial model as a face recognition model; if not, determining that the test processing fails, adjusting the training parameters, and continuing to perform training processing based on the training set until a face recognition model is obtained. The face recognition model comprises the living body detection model and the face comparison model, and the training processing is carried out by adopting the methods described in the steps S100-2 to S100-6 respectively to obtain the corresponding living body detection model and the corresponding face comparison model. Since the training process of the model is well known to those skilled in the art, it will not be further described here.
Further, in order to use the spatial domain format user image of the user to be checked in the subsequent related service processing process or in the related service tracing process, in one or more embodiments of the present specification, as shown in fig. 7, after step S106, the method further includes:
step S108, if the identity verification result information is that the verification is passed, an encrypted template image is obtained;
optionally, a fixed encrypted template image is preset, and the preset encrypted template image is obtained from the designated position. Or randomly grabbing an image from the network, and determining the grabbed image as an encrypted template image. Alternatively, an image is randomly selected from a designated gallery, and the selected image is determined as an encrypted template image. In this specification, the manner of obtaining the encryption template image and the pattern of the encryption template image are not particularly limited, and may be set by itself as needed in practical applications.
Step S110, carrying out encryption processing on a target image by adopting an encryption template image;
specifically, the encryption template image and the target image are subjected to fusion processing, and the fused image is determined as the encrypted image of the target image.
In step S112, the encrypted target image is saved.
Optionally, the target image after the encryption processing is saved to a local designated storage area of the identity verification device, or the target image after the encryption processing is saved to a cloud, and the like. When the encrypted template image is an image randomly captured from a network or an image randomly selected from a designated gallery, the method further comprises the steps of storing the relevant information of the encrypted template image and the target image in a related manner, so that when the user image in the airspace format needs to be restored, the encrypted template image is obtained according to the relevant information of the target image, and the encrypted target image is decrypted according to the encrypted template image to obtain the target image; and carrying out format conversion processing on the target image to obtain a user image in a spatial domain format. Wherein, the related information of the target image is the image identification of the target image.
Therefore, even if an attacker intercepts the target image after the encryption processing, the attacker cannot decrypt the target image after the encryption processing because the attacker does not know the encryption template image by performing the encryption processing on the target image by using the encryption template image and storing the target image after the encryption processing. Therefore, the dual protection of the user image in the space domain format is realized, and the safety of the user privacy information is greatly improved.
In one or more embodiments of the present specification, a preset encryption algorithm may be further used to perform encryption processing on the target image, specifically, as shown in fig. 8, after step S106, the method may further include:
step S114, if the identity verification result information is that the verification is passed, encrypting the target image according to a preset encryption algorithm;
wherein, the encryption algorithm, such as AES encryption algorithm, MD5 encryption algorithm, etc., can be set by itself as required in practical application.
Step S116, the encrypted target image is saved.
Therefore, the target image is encrypted by adopting the preset encryption algorithm, and the encrypted target image is stored, so that even if an attacker intercepts the encrypted target image, the attacker cannot decrypt the encrypted target image because the attacker does not know the encryption algorithm of the object. Therefore, the dual protection of the user image in the space domain format is realized, and the safety of the user privacy information is greatly improved.
In one or more embodiments of the present description, an identity verification process is performed by converting a user image in a spatial domain format into a target image in a frequency domain format and performing identity verification processing based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
Based on the same technical concept, the identity verification method based on privacy protection described in correspondence with fig. 2 to 8 above also provides an identity verification device based on privacy protection according to one or more embodiments of the present specification. Fig. 9 is a schematic block diagram illustrating an identity verification apparatus based on privacy protection according to one or more embodiments of the present disclosure, the apparatus being configured to perform the identity verification method based on privacy protection described in fig. 2 to 8, and as shown in fig. 9, the apparatus includes:
the acquisition module 201 acquires a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
the conversion module 202 is configured to perform format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and the verification module 203 is used for verifying the identity of the user to be verified according to the target image and the pre-trained face recognition model to obtain identity verification result information.
In the identity verification device based on privacy protection provided by one or more embodiments of the present specification, a user image in a spatial domain format is converted into a target image in a frequency domain format, and identity verification processing is performed based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
Optionally, the conversion module 202 is configured to pre-process the user image to obtain an image to be converted; and the number of the first and second groups,
and carrying out format conversion processing on the image to be converted according to a preset mode to obtain a target image in a frequency domain format.
Optionally, the conversion module 202 performs face detection processing on the user image according to a preset detection algorithm to obtain key position information of the face; and the number of the first and second groups,
and according to the key position information, calibrating the user image to obtain an image to be converted.
Optionally, the verification module 203 performs living body detection processing on the target image according to a pre-trained living body detection model;
and if the result of the living body detection processing is that the detection is passed, carrying out face comparison processing on the target image according to a pre-trained face comparison model.
Optionally, the apparatus further comprises: a first saving module;
the storage module is used for acquiring an encrypted template image if the identity verification result information is that the identity verification passes;
encrypting the target image by adopting the encryption template image;
and storing the encrypted target image.
Optionally, the apparatus further comprises: a second saving module;
the second storage module is used for encrypting the target image according to a preset encryption algorithm if the identity verification result information is verified to pass; and the number of the first and second groups,
and storing the encrypted target image.
Optionally, the apparatus further comprises: a training module;
the training module acquires a sample set to be trained before the verification module verifies the identity of the user to be verified according to the target image and a pre-trained face recognition model; wherein the sample set comprises a plurality of sample images in a spatial format; and the number of the first and second groups,
carrying out format conversion processing on each sample image in the sample set according to the preset mode to obtain a sample image in a frequency domain format;
and performing model training processing based on the sample image in the frequency domain format to obtain the face recognition model.
In the identity verification device based on privacy protection provided by one or more embodiments of the present specification, a user image in a spatial domain format is converted into a target image in a frequency domain format, and identity verification processing is performed based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
It should be noted that, the embodiment of the identity verification apparatus based on privacy protection in this specification and the embodiment of the identity verification method based on privacy protection in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the aforementioned corresponding identity verification method based on privacy protection, and repeated details are omitted.
Further, corresponding to the above-described identity verification method based on privacy protection, based on the same technical concept, one or more embodiments of the present specification further provide an identity verification device based on privacy protection, where the device is configured to perform the above-described identity verification method, and fig. 10 is a schematic structural diagram of an identity verification device based on privacy protection provided in one or more embodiments of the present specification.
As shown in fig. 10, the privacy-preserving identity verification device may have a relatively large difference due to different configurations or performances, and may include one or more processors 301 and a memory 302, where the memory 302 may store one or more stored applications or data. Memory 302 may be, among other things, transient storage or persistent storage. The application program stored in memory 302 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a privacy-based identity verification device. Still further, the processor 301 may be configured to communicate with the memory 302 to execute a series of computer-executable instructions in the memory 302 on a privacy-based identity verification device. The privacy-preserving based identity verification apparatus may also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input-output interfaces 305, one or more keyboards 306, and the like.
In a particular embodiment, a privacy-based identity verification apparatus 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 privacy-based identity verification apparatus, and the one or more programs configured to be executed by one or more processors include computer-executable instructions for:
acquiring a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
In the identity verification device based on privacy protection provided in one or more embodiments of the present specification, a user image in a spatial domain format is converted into a target image in a frequency domain format, and identity verification processing is performed based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
Optionally, when executed, the computer-executable instructions perform format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format, where the format conversion processing includes:
preprocessing the user image to obtain an image to be converted;
and carrying out format conversion processing on the image to be converted according to a preset mode to obtain a target image in a frequency domain format.
Optionally, when executed, the computer-executable instructions perform preprocessing on the user image to obtain an image to be converted, including:
according to a preset detection algorithm, carrying out face detection processing on the user image to obtain key position information of the face;
and according to the key position information, calibrating the user image to obtain an image to be converted.
Optionally, when executed, the computer-executable instructions perform verification processing on the identity of the user to be verified according to the target image and a pre-trained face recognition model, including:
performing living body detection processing on the target image according to a pre-trained living body detection model;
and if the result of the living body detection processing is that the detection is passed, carrying out face comparison processing on the target image according to a pre-trained face comparison model.
Optionally, the computer executable instructions, when executed, further include, after obtaining the identity verification result information:
if the identity verification result information is that verification is passed, acquiring an encrypted template image;
encrypting the target image by adopting the encryption template image;
and storing the encrypted target image.
Optionally, the computer executable instructions, when executed, further include, after obtaining the identity verification result information:
if the identity verification result information is that the verification is passed, encrypting the target image according to a preset encryption algorithm;
and storing the encrypted target image.
Optionally, when executed, before performing verification processing on the identity of the user to be verified according to the target image and the pre-trained face recognition model, the computer-executable instructions further include:
acquiring a sample set to be trained; wherein the sample set comprises a plurality of sample images in a spatial format;
carrying out format conversion processing on each sample image in the sample set according to the preset mode to obtain a sample image in a frequency domain format;
and performing model training processing based on the sample image in the frequency domain format to obtain the face recognition model.
In the identity verification device based on privacy protection provided in one or more embodiments of the present specification, a user image in a spatial domain format is converted into a target image in a frequency domain format, and identity verification processing is performed based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
It should be noted that, the embodiment of the identity verification device based on privacy protection in this specification and the embodiment of the identity verification method based on privacy protection in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the foregoing corresponding identity verification method based on privacy protection, and repeated details are omitted.
Further, corresponding to the above-described identity verification method based on privacy protection, based on the same technical concept, 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 when being executed by a processor, the storage medium stores computer-executable instructions capable of implementing the following processes:
acquiring a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
One or more embodiments of the present description provide a storage medium storing computer-executable instructions that, when executed by a processor, perform identity verification processing by converting a user image in a spatial domain format into a target image in a frequency domain format and based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium perform format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format, where the format conversion processing includes:
preprocessing the user image to obtain an image to be converted;
and carrying out format conversion processing on the image to be converted according to a preset mode to obtain a target image in a frequency domain format.
Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium perform preprocessing on the user image to obtain an image to be converted, including:
according to a preset detection algorithm, carrying out face detection processing on the user image to obtain key position information of the face;
and according to the key position information, calibrating the user image to obtain an image to be converted.
Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium perform verification processing on the identity of the user to be verified according to the target image and a pre-trained face recognition model, and include:
performing living body detection processing on the target image according to a pre-trained living body detection model;
and if the result of the living body detection processing is that the detection is passed, carrying out face comparison processing on the target image according to a pre-trained face comparison model.
Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, further comprise, after obtaining the identity verification result information:
if the identity verification result information is that verification is passed, acquiring an encrypted template image;
encrypting the target image by adopting the encryption template image;
and storing the encrypted target image.
Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, further comprise, after obtaining the identity verification result information:
if the identity verification result information is that the verification is passed, encrypting the target image according to a preset encryption algorithm;
and storing the encrypted target image.
Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium, before performing verification processing on the identity of the user to be verified according to the target image and the pre-trained face recognition model, further include:
acquiring a sample set to be trained; wherein the sample set comprises a plurality of sample images in a spatial format;
carrying out format conversion processing on each sample image in the sample set according to the preset mode to obtain a sample image in a frequency domain format;
and performing model training processing based on the sample image in the frequency domain format to obtain the face recognition model.
One or more embodiments of the present description provide a storage medium storing computer-executable instructions that, when executed by a processor, perform identity verification processing by converting a user image in a spatial domain format into a target image in a frequency domain format and based on the target image in the frequency domain format; because the target image in the frequency domain format cannot embody the image content of the user image in the airspace format, even if the target image is stolen by an attacker in the identity verification processing process, the attacker cannot know the image content of the user image in the airspace format, so that the safety of user privacy information is effectively guaranteed, and the risks of user property loss and the like caused by leakage of the user privacy information are avoided.
It should be noted that, the embodiment of the storage medium in this specification and the embodiment of the identity verification method based on privacy protection in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the foregoing corresponding identity verification method based on privacy protection, and repeated parts 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), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. 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 functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.
One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments 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, the 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 therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. 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 embodiments of the present description may 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 embodiments of the specification may 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 only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.
Claims (13)
1. An identity verification method based on privacy protection comprises the following steps:
acquiring a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
2. The method according to claim 1, wherein the performing format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format comprises:
preprocessing the user image to obtain an image to be converted;
and carrying out format conversion processing on the image to be converted according to a preset mode to obtain a target image in a frequency domain format.
3. The method of claim 2, wherein the pre-processing the user image to obtain an image to be converted comprises:
according to a preset detection algorithm, carrying out face detection processing on the user image to obtain key position information of the face;
and according to the key position information, calibrating the user image to obtain an image to be converted.
4. The method according to claim 1, wherein the verifying the identity of the user to be verified according to the target image and a pre-trained face recognition model comprises:
performing living body detection processing on the target image according to a pre-trained living body detection model;
and if the result of the living body detection processing is that the detection is passed, carrying out face comparison processing on the target image according to a pre-trained face comparison model.
5. The method of claim 1, after obtaining the identity verification result information, further comprising:
if the identity verification result information is that verification is passed, acquiring an encrypted template image;
encrypting the target image by adopting the encryption template image;
and storing the encrypted target image.
6. The method of claim 1, after obtaining the identity verification result information, further comprising:
if the identity verification result information is that the verification is passed, encrypting the target image according to a preset encryption algorithm;
and storing the encrypted target image.
7. The method according to any one of claims 1 to 6, wherein before the verification processing of the identity of the user to be verified according to the target image and the pre-trained face recognition model, the method further comprises:
acquiring a sample set to be trained; wherein the sample set comprises a plurality of sample images in a spatial format;
carrying out format conversion processing on each sample image in the sample set according to the preset mode to obtain a sample image in a frequency domain format;
and performing model training processing based on the sample image in the frequency domain format to obtain the face recognition model.
8. An identity verification device based on privacy protection, comprising:
the acquisition module acquires a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
the conversion module is used for carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and the verification module is used for verifying the identity of the user to be verified according to the target image and the pre-trained face recognition model to obtain identity verification result information.
9. The apparatus of claim 8, wherein the first and second electrodes are disposed on opposite sides of the substrate,
the conversion module is used for preprocessing the user image to obtain an image to be converted;
and carrying out format conversion processing on the image to be converted according to a preset mode to obtain a target image in a frequency domain format.
10. The apparatus of claim 8, wherein the first and second electrodes are disposed on opposite sides of the substrate,
the verification module is used for carrying out in-vivo detection processing on the target image according to a pre-trained in-vivo detection model;
and if the result of the living body detection processing is that the detection is passed, carrying out face comparison processing on the target image according to a pre-trained face comparison model.
11. The apparatus of claim 8, the apparatus further comprising: a first saving module;
the first storage module is used for acquiring an encrypted template image when the identity verification result information is that the identity verification passes;
encrypting the target image by adopting the encryption template image;
and storing the encrypted target image.
12. An identity verification device based on privacy protection, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
13. A storage medium storing computer-executable instructions that when executed implement the following:
acquiring a user image in an airspace format of a user to be checked; wherein the user image comprises a face of the user to be verified;
carrying out format conversion processing on the user image according to a preset mode to obtain a target image in a frequency domain format;
and according to the target image and a pre-trained face recognition model, verifying the identity of the user to be verified to obtain identity verification result information.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010462097.3A CN111538968A (en) | 2020-05-27 | 2020-05-27 | Identity verification method, device and equipment based on privacy protection |
PCT/CN2021/095935 WO2021238956A1 (en) | 2020-05-27 | 2021-05-26 | Identity verification method, apparatus and device based on privacy protection |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010462097.3A CN111538968A (en) | 2020-05-27 | 2020-05-27 | Identity verification method, device and equipment based on privacy protection |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111538968A true CN111538968A (en) | 2020-08-14 |
Family
ID=71978009
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010462097.3A Pending CN111538968A (en) | 2020-05-27 | 2020-05-27 | Identity verification method, device and equipment based on privacy protection |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN111538968A (en) |
WO (1) | WO2021238956A1 (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112398838A (en) * | 2020-11-06 | 2021-02-23 | 支付宝(杭州)信息技术有限公司 | Authentication method, device and equipment based on privacy protection |
CN112507934A (en) * | 2020-12-16 | 2021-03-16 | 平安银行股份有限公司 | Living body detection method, living body detection device, electronic apparatus, and storage medium |
CN113343295A (en) * | 2021-06-07 | 2021-09-03 | 支付宝(杭州)信息技术有限公司 | Image processing method, device, equipment and storage medium based on privacy protection |
WO2021238956A1 (en) * | 2020-05-27 | 2021-12-02 | 支付宝(杭州)信息技术有限公司 | Identity verification method, apparatus and device based on privacy protection |
CN113850717A (en) * | 2021-11-30 | 2021-12-28 | 北京爱笔科技有限公司 | Image processing method and device |
CN114913565A (en) * | 2021-01-28 | 2022-08-16 | 腾讯科技(深圳)有限公司 | Face image detection method, model training method, device and storage medium |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114338241B (en) * | 2022-03-10 | 2023-01-24 | 成都网讯优速信息技术有限公司 | Data encryption and decryption method and device and network router adopting device |
CN114866345B (en) * | 2022-07-05 | 2022-12-09 | 支付宝(杭州)信息技术有限公司 | Processing method, device and equipment for biological recognition |
CN115564205A (en) * | 2022-09-26 | 2023-01-03 | 重庆大学 | Intelligent warehouse management method and system for power transformation operation and maintenance |
CN116308888B (en) * | 2023-05-19 | 2023-08-11 | 南方电网数字平台科技(广东)有限公司 | Operation ticket management system based on neural network |
CN117710510B (en) * | 2024-02-04 | 2024-06-11 | 支付宝(杭州)信息技术有限公司 | Image generation method and device |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102831406A (en) * | 2012-08-17 | 2012-12-19 | 广东非思智能科技股份有限公司 | Human face identification method based on spatial filters |
CN105184236A (en) * | 2015-08-26 | 2015-12-23 | 江苏久祥汽车电器集团有限公司 | Robot-based face identification system |
CN107248135A (en) * | 2017-04-26 | 2017-10-13 | 阿里巴巴集团控股有限公司 | Generation, recognition methods and device, the computer-readable storage medium of anti-counterfeiting image |
CN107798282A (en) * | 2016-09-07 | 2018-03-13 | 北京眼神科技有限公司 | Method and device for detecting human face of living body |
US20180144184A1 (en) * | 2016-11-24 | 2018-05-24 | Bioid Ag | Biometric method |
CN110462633A (en) * | 2019-06-27 | 2019-11-15 | 深圳市汇顶科技股份有限公司 | A kind of method, apparatus and electronic equipment of recognition of face |
CN110633650A (en) * | 2019-08-22 | 2019-12-31 | 首都师范大学 | Convolutional neural network face recognition method and device based on privacy protection |
CN110705392A (en) * | 2019-09-17 | 2020-01-17 | Oppo广东移动通信有限公司 | Face image detection method and device and storage medium |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103152556A (en) * | 2013-03-15 | 2013-06-12 | 云南大学 | Surveillance video encryption method for protecting privacy |
CN107578036A (en) * | 2017-09-28 | 2018-01-12 | 南通大学 | A kind of depth image tumble recognizer based on wavelet moment |
CN110598584A (en) * | 2019-08-26 | 2019-12-20 | 天津大学 | Convolutional neural network face recognition algorithm based on wavelet transform and DCT |
CN111538968A (en) * | 2020-05-27 | 2020-08-14 | 支付宝(杭州)信息技术有限公司 | Identity verification method, device and equipment based on privacy protection |
-
2020
- 2020-05-27 CN CN202010462097.3A patent/CN111538968A/en active Pending
-
2021
- 2021-05-26 WO PCT/CN2021/095935 patent/WO2021238956A1/en active Application Filing
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102831406A (en) * | 2012-08-17 | 2012-12-19 | 广东非思智能科技股份有限公司 | Human face identification method based on spatial filters |
CN105184236A (en) * | 2015-08-26 | 2015-12-23 | 江苏久祥汽车电器集团有限公司 | Robot-based face identification system |
CN107798282A (en) * | 2016-09-07 | 2018-03-13 | 北京眼神科技有限公司 | Method and device for detecting human face of living body |
US20180144184A1 (en) * | 2016-11-24 | 2018-05-24 | Bioid Ag | Biometric method |
CN107248135A (en) * | 2017-04-26 | 2017-10-13 | 阿里巴巴集团控股有限公司 | Generation, recognition methods and device, the computer-readable storage medium of anti-counterfeiting image |
CN110462633A (en) * | 2019-06-27 | 2019-11-15 | 深圳市汇顶科技股份有限公司 | A kind of method, apparatus and electronic equipment of recognition of face |
CN110633650A (en) * | 2019-08-22 | 2019-12-31 | 首都师范大学 | Convolutional neural network face recognition method and device based on privacy protection |
CN110705392A (en) * | 2019-09-17 | 2020-01-17 | Oppo广东移动通信有限公司 | Face image detection method and device and storage medium |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2021238956A1 (en) * | 2020-05-27 | 2021-12-02 | 支付宝(杭州)信息技术有限公司 | Identity verification method, apparatus and device based on privacy protection |
CN112398838A (en) * | 2020-11-06 | 2021-02-23 | 支付宝(杭州)信息技术有限公司 | Authentication method, device and equipment based on privacy protection |
CN112398838B (en) * | 2020-11-06 | 2022-09-23 | 支付宝(杭州)信息技术有限公司 | Authentication method, device, equipment and storage medium based on privacy protection |
CN112507934A (en) * | 2020-12-16 | 2021-03-16 | 平安银行股份有限公司 | Living body detection method, living body detection device, electronic apparatus, and storage medium |
CN112507934B (en) * | 2020-12-16 | 2024-06-07 | 平安银行股份有限公司 | Living body detection method, living body detection device, electronic equipment and storage medium |
CN114913565A (en) * | 2021-01-28 | 2022-08-16 | 腾讯科技(深圳)有限公司 | Face image detection method, model training method, device and storage medium |
CN114913565B (en) * | 2021-01-28 | 2023-11-17 | 腾讯科技(深圳)有限公司 | Face image detection method, model training method, device and storage medium |
CN113343295A (en) * | 2021-06-07 | 2021-09-03 | 支付宝(杭州)信息技术有限公司 | Image processing method, device, equipment and storage medium based on privacy protection |
CN113343295B (en) * | 2021-06-07 | 2023-01-24 | 支付宝(杭州)信息技术有限公司 | Image processing method, device, equipment and storage medium based on privacy protection |
CN113850717A (en) * | 2021-11-30 | 2021-12-28 | 北京爱笔科技有限公司 | Image processing method and device |
Also Published As
Publication number | Publication date |
---|---|
WO2021238956A1 (en) | 2021-12-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111538968A (en) | Identity verification method, device and equipment based on privacy protection | |
KR102142232B1 (en) | Face liveness detection method and apparatus, and electronic device | |
WO2021184898A1 (en) | Facial feature extraction method, apparatus and device | |
US20210319098A1 (en) | Securing systems employing artificial intelligence | |
US11444774B2 (en) | Method and system for biometric verification | |
Raval et al. | Markit: Privacy markers for protecting visual secrets | |
CN108055132B (en) | Method, device and equipment for service authorization | |
US11232232B1 (en) | Image privacy protection method, apparatus and device | |
US20110033090A1 (en) | Reflexive iris template | |
US9594969B1 (en) | Iris recognition including liveness testing | |
CN112417414A (en) | Privacy protection method, device and equipment based on attribute desensitization | |
CN111340014B (en) | Living body detection method, living body detection device, living body detection apparatus, and storage medium | |
KR100905675B1 (en) | Arraratus and method for recognizing fingerprint | |
CN112084476A (en) | Biological identification identity verification method, client, server, equipment and system | |
CN111160251B (en) | Living body identification method and device | |
CN115362440A (en) | Authentication and calibration via gaze tracking | |
Chavez-Galaviz et al. | Embedded biometric cryptosystem based on finger vein patterns | |
CN113343295B (en) | Image processing method, device, equipment and storage medium based on privacy protection | |
CN112837202B (en) | Watermark image generation and attack tracing method and device based on privacy protection | |
CN112600886B (en) | Privacy protection method, device and equipment with combination of end cloud and device | |
CN113239852A (en) | Privacy image processing method, device and equipment based on privacy protection | |
CN117560455B (en) | Image feature processing method, device, equipment and storage medium | |
CN113239851B (en) | Privacy image processing method, device and equipment based on privacy protection | |
Hauer | Continuous Supervision: A Novel Concept for Enhancing Data Leakage Prevention | |
CN114826689B (en) | Information input method, security authentication method and electronic equipment |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40035498 Country of ref document: HK |
|
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200814 |