CN114419713A - Face recognition auxiliary method, face recognition method and terminal equipment - Google Patents
Face recognition auxiliary method, face recognition method and terminal equipment Download PDFInfo
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- CN114419713A CN114419713A CN202210068057.XA CN202210068057A CN114419713A CN 114419713 A CN114419713 A CN 114419713A CN 202210068057 A CN202210068057 A CN 202210068057A CN 114419713 A CN114419713 A CN 114419713A
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Abstract
The application provides a face recognition auxiliary method, a face recognition method and a terminal device, wherein the face recognition auxiliary method comprises the following steps: acquiring a face image to be recognized; acquiring the face distribution in the face image; and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image.
Description
This patent application is application number: 2018110274913, filing date: in 2018, 9, 4.8, a divisional application of a chinese patent application entitled "an auxiliary method for face recognition, a face recognition method, and a terminal device" was invented.
Technical Field
The embodiment of the specification relates to the technical field of face recognition, in particular to a face recognition auxiliary method, a face recognition method and terminal equipment.
Background
With the rapid development of various payment technologies, face payment is produced in order to greatly simplify the payment process. Face payment is a new electronic payment method, which consists of two parts: and the face recognition logs in the user wallet account and deducts money from the wallet to complete the payment process. The process of logging in the wallet account of the user through face recognition is to scan and/or shoot a face picture of the user, and the face picture is compared with a reserved picture in the wallet account of the user to complete the identity recognition and verification of the user, so that the process of deducting money from the wallet and completing payment is completed. However, in the current face payment mode, during the process of scanning and/or shooting the face picture of the user, a plurality of interference factors influence the selection of the real user, so that the accuracy of the user selection is influenced, and the safety is poor.
Disclosure of Invention
The embodiment of the specification provides a face recognition auxiliary method, a face recognition method and terminal equipment, which are used for prompting and guiding a user to remove interference factors, so that the accuracy of face image selection of the user is improved.
The embodiment of the specification adopts the following technical scheme:
in a first aspect, an auxiliary method for face recognition is provided, including:
acquiring a face image to be recognized;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image.
In a second aspect, a face recognition method is provided, including:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
In a third aspect, a terminal device is provided, which includes:
the first acquisition module is used for acquiring a face image to be recognized;
the second acquisition module is used for acquiring the face distribution in the face image;
and the execution module is used for executing the reminding operation corresponding to the face distribution based on the face distribution in the face image when the face distribution is abnormal.
In a fourth aspect, a terminal device is provided, which includes:
the system comprises an acquisition module, a display module and a processing module, wherein the acquisition module is used for acquiring images comprising a plurality of human faces;
a selection module for selecting at least one face image from the images of the plurality of faces;
the comparison module is used for comparing the at least one face image with the face image of the target user;
and the determining module is used for determining whether the identification is successful or not based on the comparison result.
In a fifth aspect, a terminal device is provided, which includes: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:
acquiring a face image to be recognized;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image.
In a sixth aspect, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring a face image to be recognized;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image.
In a seventh aspect, a terminal device is provided, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
In an eighth aspect, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the method and the device, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of face image selection of the user is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an auxiliary method for face recognition according to an embodiment of the present disclosure;
fig. 2 is a schematic view of an implementation scenario of an actual application of the auxiliary method for face recognition provided in an embodiment of the present specification;
fig. 3 is a system block diagram in a practical application scenario of the auxiliary method for face recognition provided in an embodiment of the present specification;
fig. 4 is a flowchart of a face recognition method according to an embodiment of the present disclosure;
fig. 5 is one of block diagrams of a terminal device according to an embodiment of the present specification;
fig. 6 is a second block diagram of a terminal device according to an embodiment of the present disclosure;
fig. 7 is a third block diagram of a terminal device according to an embodiment of the present disclosure;
fig. 8 is a fourth block diagram of a terminal device according to an embodiment of the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a face recognition auxiliary method and terminal equipment, which are used for prompting and guiding a user to remove interference factors, so that the accuracy of face image selection of the user is improved. The embodiment of the present application provides an auxiliary method for face recognition, and an execution subject of the method may be, but is not limited to, a terminal device or an apparatus or system capable of being configured to execute the method provided by the embodiment of the present application.
For convenience of description, the following description will be made of an embodiment of the method taking as an example that an execution subject of the method is a terminal device capable of executing the method. It is understood that the implementation of the method by the terminal device is only an exemplary illustration, and should not be construed as a limitation of the method.
Fig. 1 is a flowchart of an auxiliary method for face recognition according to an embodiment of the present application, where the method in fig. 1 may be executed by a terminal device, and as shown in fig. 1, the method may include:
and step 110, obtaining a face image to be recognized.
The implementation manner of acquiring the face image to be recognized may be to acquire the face image to be recognized in a scanning manner, or to acquire the face image to be recognized in a shooting manner. The embodiments of the present application are not particularly limited.
And step 120, acquiring the face distribution in the face image.
The obtaining of the face distribution in the face image may specifically be: detecting whether the obtained face image contains a face or not through a preset algorithm; if yes, detecting the number of the human faces; in addition, whether the acquired face image contains an incomplete face image or not can be detected through a preset algorithm.
And step 130, when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image.
The face distribution anomaly may include: the method comprises the following steps that at least one of at least two faces do not exist in the face image, at least two faces with the largest size exist in the face image, and feature information of the faces in the face image does not meet preset conditions.
According to the method and the device, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of face image selection of the user is improved.
Optionally, as an embodiment, the step 120 may be specifically implemented as:
taking the face image as the input of a plurality of face detection models to obtain the output face distribution;
the multi-face detection model is obtained by training based on face image samples with face distribution abnormity.
The face image samples with face distribution abnormality may include at least one of face image samples without faces, face image samples with at least two faces, and face image samples with incomplete faces.
It is assumed that the face image samples with abnormal face distribution include face image samples without faces and face image samples with at least two faces, and the face image samples with normal face distribution include face image samples with one face.
In this step, the multiple face detection models may be obtained as follows: firstly, training data probably comprises 3 types of face image samples, namely a face image sample without a face, a face image sample with at least two faces and a face image sample with one face, and one thousand images are respectively selected under the same type; then, a plurality of face detection models are obtained through training of one thousand face image samples in 3 classes. How to obtain a plurality of face detection models through training of each one thousand face image samples of 3 categories belongs to the prior art, and is not repeated in the embodiment of the application.
According to the embodiment of the application, the multiple face detection models are obtained through face image sample training with face distribution abnormity, then the face images are used as input of the multiple face detection models to obtain output face distribution, whether interference factors exist in the face images is determined according to whether the face distribution is abnormal, the influence of the interference factors existing in the collected face images is effectively avoided, the success rate of subsequently adopting the face images to perform user identity authentication and face payment is ensured, and the usability and the safety of a face scanning payment process are ensured.
Optionally, as an embodiment, if the face distribution abnormality includes that no face exists in the face image, executing a reminding operation corresponding to the face distribution, where the reminding operation includes:
and reminding the user that the user is located in an image acquisition area of an image acquisition device of the terminal equipment based on the fact that the face does not exist in the face image.
It should be understood that after step 120 is executed, the method for assisting in face recognition provided by the embodiment of the present application further includes: determining whether the face distribution is abnormal, namely determining whether a face exists in the face image; if so, determining whether at least two faces exist in the face image; and if the operation does not exist, the reminding operation is an operation for reminding the user that the user is located in the image acquisition area of the image acquisition device of the terminal equipment.
In the embodiment of the application, if the face does not exist in the face image, a reminding operation corresponding to face distribution is executed to remind a user that the face is not detected and guide the user to adjust the standing position, so that the face image of the user is accurately acquired.
It should be understood that when a plurality of faces exist in the image capturing area, the face recognition may or may not be interfered.
Optionally, as an embodiment, if the face distribution abnormality includes that at least two faces exist in the face image, executing a reminding operation corresponding to the face distribution, where the reminding operation includes:
and if at least two faces exist in the face image and the size difference of the two largest faces is smaller than a preset threshold value, reminding the user to ensure that other users do not exist in the acquisition area of the image acquisition device of the terminal equipment.
It should be understood that after step 120 is executed, the method for assisting in face recognition provided by the embodiment of the present application further includes: determining whether at least two faces exist in the face image; and if so, determining whether the difference between the sizes of the two faces with the largest size in the face image is larger than a preset threshold value. If the difference between the sizes of the two faces with the largest size in the face image is larger than the preset threshold value, the reminding operation is to remind a user to ensure that the operation of other users does not exist in the acquisition area of the image acquisition device of the terminal equipment. For example, remind the user "to ensure payment is safe, please ensure that there are no other people around you when brushing the face".
For example, if a user stands side by side with other users (such as friends, relatives, or strangers) in an image capturing area of an image capturing device of a terminal device, at least two faces with the largest size exist in a face image captured by the image capturing device.
According to the embodiment of the application, when two largest faces with similar sizes exist in the face image, the user is reminded of other users around the user, and the user is guided to clear away other users around the user, so that the accuracy of the obtained face image is ensured, and the safety of face scanning payment is ensured.
Optionally, as an embodiment, if the face distribution abnormality includes that at least two faces exist in the face image, executing a reminding operation corresponding to the face distribution, where the reminding operation includes:
selecting a face with the largest size in the face image;
determining whether the characteristic information of the face with the largest size meets face recognition comparison conditions;
and if not, reminding the user to keep a preset distance from the image acquisition device of the terminal equipment.
For example, if a user stands in front of or behind another user (such as a friend, a relative, or a stranger) in an image capturing area of an image capturing device of the terminal device, at least two faces exist in a face image captured by the image capturing device. Due to the fact that the distance from the image acquisition device is different, the sizes of the at least two faces are different. At this time, since the user is located at the closest position of the image capturing device in the actual operation, the size of the face image of the user is usually the largest.
The feature information of the face may refer to size information of the face, position information of the face on a terminal screen, and the like. Accordingly, the face recognition comparison condition is used to represent a condition satisfying the face recognition comparison, for example, the face recognition comparison condition is a size range of the face.
The face with the largest size is the face with the largest size after weighting, and the face with the largest size after weighting is obtained based on the size of a face detection frame and the distance from the center of the face detection frame to the center of a screen of a terminal device. It should be understood that, based on the size of the face detection frame, the result obtained by inversely weighting the distance from the center of the face detection frame to the center of the screen of the terminal device (i.e., the smaller the distance from the center of the face detection frame to the center of the screen, the greater the weight, the smaller the distance, the less the weight), is represented by the following mathematical expression: and after weighting, the size of the face is equal to the size of the face detection frame/the distance from the center of the face detection frame to the center of the screen.
The method comprises the steps of selecting a face with the largest size in a face image; determining whether the characteristic information of the face with the largest size meets face recognition comparison conditions; if the face image does not meet the preset distance, reminding the user of keeping the preset distance with the image acquisition device of the terminal equipment to remind the user that the selected face image does not meet the face recognition comparison condition, and guiding the user to adjust the position of the image acquisition device in the image acquisition area and the distance from the image acquisition device so as to encourage the user to be positioned at the appointed position of the image acquisition device in the image acquisition area during face scanning, so that the subsequent processing is facilitated.
Optionally, as an embodiment, when the detection result of the specified detection operation is normal, the method for assisting face recognition provided in the embodiment of the present application may further include:
determining whether a face exists in the face image; if so, determining whether the characteristic information of the face meets face identification comparison conditions; if the distance does not meet the preset distance, reminding the user to keep the preset distance with an image acquisition device of the terminal equipment; and if so, sending the face image to be recognized to recognition terminal equipment.
The method can be understood as that whether the characteristic information of the face meets the face recognition comparison condition is determined; and if so, sending the face image to be recognized to the recognition terminal equipment. The recognition terminal equipment can compare the face image with a face image stored in advance, and if the similarity value of the face image and the face image is larger than a preset value, the user identity authentication is determined to pass and money is deducted from a wallet to complete the payment operation. The preset value needs to be set according to actual requirements, and the embodiment of the application is not particularly limited.
Illustratively, the identification terminal device compares the face image with a face image stored in advance, and the comparison can be specifically realized as follows; the method comprises the steps of obtaining image information of a face area of a face image and image information of a face area of a pre-stored face image, comparing the two image information, and determining a similarity value of the face image and the pre-stored face image based on similar features in the two image information. The pre-stored face image can be a face image which is pre-stored in the identification terminal device and corresponds to a user wallet account, or a face image which is acquired in an official website system according to a user identity card number corresponding to the user wallet account.
According to the embodiment of the application, when the face distribution of the face image is normal, the face image to be recognized is sent to the recognition terminal device, and the recognition terminal device performs user identity authentication and face payment based on the face image to be recognized, so that the success rate of performing user identity authentication and face payment by adopting the face image is ensured.
The method of the embodiments of the present application will be further described with reference to specific embodiments.
Fig. 2 shows a flowchart of an auxiliary method for face recognition provided in the embodiment of the present application in an actual application scenario; fig. 3 shows a system block diagram of the auxiliary method for face recognition provided by the embodiment of the present application in an actual application scenario;
illustratively, the user performs face payment by logging in the user wallet account through face recognition, as shown in fig. 2 and 3:
at 200, the terminal device 1 prompts the user to enter a user phone number. After the user inputs the mobile phone number on the terminal device 1, the terminal device 1 sends the mobile phone number of the user to the identification terminal device.
At 210, the identification terminal device 2 receives the user mobile phone number, searches for the user wallet account based on the user mobile phone number, and if the user wallet account is found, executes step 230; otherwise, step 220 is performed.
At 220, the identification terminal device 2 prompts the user for a new user registration.
At 230, the terminal device 1 acquires a face image.
At 240, the terminal device 1 acquires a face image to be recognized.
At 250, the terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, for example, whether a face exists in the face image; if yes, go to step 260; if not, go to step 251.
The terminal device 1 obtains face distribution in a face image, and determines whether the face distribution in the face image is abnormal, for example, whether a face image exists in the face image may be specifically realized by taking part in related contents in the above embodiments, and details are not repeated in the embodiments of the present application.
At 251, the terminal device 1 performs a reminder operation corresponding to the face distribution, illustratively, reminding the user that the user is located in the image acquisition area of the image acquisition apparatus of the terminal device.
At 260, the terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, for example, whether at least two faces exist in the face image; if so, go to step 270.
At 270, the terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, for example, whether at least two faces with the largest size exist in the face image; if not, go to step 280; if yes, go to step 271.
It should be understood that the presence of at least two faces with the largest size in the face image means that the size of at least two faces with the largest size in the face image are similar.
At 271, the terminal device 1 performs a reminder operation corresponding to the face distribution, illustratively, reminding the user to ensure that no other users are present in the acquisition area of the image acquisition apparatus of the terminal device.
At 280, the terminal device 1 selects the face with the largest size in the face image, and determines whether the feature information of the face with the largest size meets the face recognition comparison condition; if not, go to step 281; otherwise, step 290 is performed.
At 281, the terminal device 1 performs a reminding operation corresponding to the face distribution, illustratively, reminding the user to keep a predetermined distance from the image capturing device of the terminal device.
At 290, the face image is compared with a pre-stored face image;
at 291, the user is authenticated and deducted from the wallet to complete the payment.
According to the method and the device, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of face image selection of the user is improved.
The embodiment of the application provides a face recognition method and terminal equipment, which are used for prompting and guiding a user to remove interference factors, so that the accuracy of face image selection of the user is improved. The embodiment of the present application provides a face recognition method, and an execution subject of the method may be, but is not limited to, a terminal device or an apparatus or system capable of being configured to execute the method provided by the embodiment of the present application.
For convenience of description, the following description will be made of an embodiment of the method taking as an example that an execution subject of the method is a terminal device capable of executing the method. It is understood that the implementation of the method by the terminal device is only an exemplary illustration, and should not be construed as a limitation of the method.
Fig. 4 is a flowchart of a face recognition method provided in an embodiment of the present application, where the method in fig. 4 may be executed by a terminal device, and as shown in fig. 4, the method may include:
The acquisition of the image including the plurality of faces may be realized by acquiring the image including the plurality of faces in a scanning manner, or acquiring the image including the plurality of faces in a shooting manner. The embodiments of the present application are not particularly limited.
The selection criterion for selecting at least one face image from the images of the faces may be set according to actual requirements, and the embodiment of the present application is not particularly limited.
And 430, comparing the at least one face image with the face image of the target user.
And step 440, determining whether the identification is successful or not based on the comparison result.
According to the embodiment of the application, the images comprising the faces are collected, at least one face image is selected from the images comprising the faces, the face images are compared with the face image of the target user based on the at least one face image, whether the face images are successfully identified is determined based on the comparison result, whether the face images which can be successfully identified exist in the images comprising the faces is identified, the success rate of user identity authentication and face payment by adopting the face images is ensured, the follow-up user can be ensured to smoothly complete the whole face payment process, and the full link passing rate is improved. Meanwhile, the method is a process for helping the user to learn to use the face payment, so that the user can feel the intelligence of the face payment and is favorable for popularization of the face payment due to unique user experience.
Optionally, as an embodiment, the step 410 may be specifically implemented as:
a plurality of faces are simultaneously acquired to obtain an image including the plurality of faces.
Illustratively, when a plurality of users are located in the image acquisition area of the image acquisition device of the terminal equipment, the human faces of the users are acquired simultaneously.
Optionally, as an embodiment, step 430 may be specifically implemented as:
and sending the identification of the target user and the characteristic information of the at least one face image to a server so as to compare the face characteristic information of the target user stored on the server through the server.
It should be understood that the server compares the feature information of the at least one facial image with the facial feature information corresponding to the identifier of the target user.
Can be realized concretely as; the method comprises the steps of obtaining feature information of at least one face image and face feature information of a target user stored in advance, comparing the two pieces of information, and determining the similarity value of the at least one face image and the face image of the target user stored in advance based on the similar features in the two pieces of information. The pre-stored face image of the target user can be a face image which is pre-stored in the identification terminal device and corresponds to a user wallet account, or a face image which is acquired in an official website system according to a user identity card number corresponding to the user wallet account.
According to the embodiment of the application, the identification of the target user and the characteristic information of the at least one face image are sent to the server, so that the face characteristic information of the target user stored on the server is compared through the server, and the identification terminal equipment performs user identity authentication and face payment based on the characteristic information of the at least one face image, so that the success rate of performing user identity authentication and face payment by adopting the face image is ensured.
Optionally, as an embodiment, step 420 may be specifically implemented as:
and if the face with the largest size exists in the images of the faces, selecting the face image with the largest size.
For example, if a user stands in front of or behind another user (such as a friend, a relative, or a stranger) in an image capturing area of an image capturing device of the terminal device, at least two faces exist in a face image captured by the image capturing device. Due to the fact that the distance from the image acquisition device is different, the sizes of the at least two faces are different. At this time, since the user is located at the closest position of the image capturing device in the actual operation, the size of the face image of the user is usually the largest.
The face with the largest size is the face with the largest size after weighting, and the face with the largest size after weighting is obtained based on the size of a face detection frame and the distance from the center of the face detection frame to the center of a screen of a terminal device. It should be understood that, based on the size of the face detection frame, the result obtained by inversely weighting the distance from the center of the face detection frame to the center of the screen of the terminal device (i.e., the smaller the distance from the center of the face detection frame to the center of the screen, the greater the weight, the smaller the distance, the less the weight), is represented by the following mathematical expression: and after weighting, the size of the face is equal to the size of the face detection frame/the distance from the center of the face detection frame to the center of the screen.
According to the embodiment of the application, the face with the largest size exists in the images of the faces, so that a user can be encouraged to be located at the specified position of the image acquisition area of the image acquisition device during face scanning, and subsequent processing is facilitated.
Optionally, as an embodiment, before performing step 420, the face recognition method provided in the embodiment of the present application further includes:
and if at least two face images exist in the images of the faces and the difference between the sizes of the two face images with the largest size in the at least two face images is smaller than a preset threshold value, executing a reminding operation.
For example, if a user stands side by side with other users (such as friends, relatives, or strangers) in an image capturing area of an image capturing device of a terminal device, at least two faces exist in a face image captured by the image capturing device, and the two faces with the largest size are close to each other.
For example, the reminding operation may be an operation for reminding the user to ensure that no other user exists in the acquisition area of the image acquisition device of the terminal device, such as reminding the user "please ensure that no other person exists around you when brushing the face to ensure the payment safety".
According to the embodiment of the application, when the fact that at least two face images exist in the face images and the two faces with the largest sizes are close to each other is determined, the reminding operation is executed to remind other users around the user, the user is guided to leave other users around the user, the accuracy of the obtained face images is ensured, and the safety of face scanning payment is ensured.
The face recognition assisting method according to the embodiment of the present application is described in detail above with reference to fig. 1 to 3, and the terminal device according to the embodiment of the present application is described in detail below with reference to fig. 5.
Fig. 5 shows a schematic structural diagram of a terminal device provided in an embodiment of the present application, and as shown in fig. 5, the terminal device 500 may include:
a first obtaining module 510, configured to obtain a face image to be recognized;
a second obtaining module 520, configured to obtain face distribution in the face image;
an executing module 530, configured to, when the face distribution is abnormal, execute a reminding operation corresponding to the face distribution based on the face distribution in the face image.
In one embodiment, the second obtaining module 520 includes:
the input unit is used for taking the face image as the input of a plurality of face detection models so as to obtain the output face distribution;
the multi-face detection model is obtained by training based on face image samples with face distribution abnormity.
In one embodiment, the face distribution anomaly comprises: the method comprises the following steps that at least one of at least two faces do not exist in the face image, at least two faces with the largest size exist in the face image, and feature information of the faces in the face image does not meet preset conditions.
In one embodiment, if the face distribution anomaly includes that no face exists in the face image, the executing module 530 includes:
and the first reminding unit is used for reminding a user that the user is located in an image acquisition area of an image acquisition device of the terminal equipment based on the fact that the face does not exist in the face image.
In one embodiment, if the face distribution anomaly includes at least two faces in the face image, the executing module 530 includes:
a first determining unit, configured to determine whether at least two faces exist in the face image and a difference between sizes of two faces with a largest size is smaller than a predetermined threshold;
and the second reminding unit is used for reminding the user to ensure that other users do not exist in the acquisition area of the image acquisition device of the terminal equipment if the first determining unit determines that at least two faces exist in the face image and the size difference between the two faces with the largest size is smaller than a preset threshold value.
In one embodiment, if the face distribution anomaly includes at least two faces in the face image, the executing module 530 includes:
the selecting unit is used for selecting the face with the largest size in the face image;
the second determining unit is used for determining whether the feature information of the face with the largest size meets face recognition comparison conditions;
and the third reminding unit is used for reminding the user of keeping a preset distance from the image acquisition device of the terminal equipment if the second determining unit determines that the feature information of the face with the largest size does not meet the face recognition comparison condition.
In an embodiment, the face with the largest size is a face with the largest size after weighting, and the face with the largest size after weighting is obtained based on the size of a face detection frame and the distance from the center of the face detection frame to the center of a screen of a terminal device.
According to the method and the device, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of face image selection of the user is improved.
The face recognition method according to the embodiment of the present application is described in detail above with reference to fig. 4, and the terminal device according to the embodiment of the present application is described in detail below with reference to fig. 6.
Fig. 6 shows a schematic structural diagram of a terminal device provided in an embodiment of the present application, and as shown in fig. 6, the terminal device 600 may include:
an acquisition module 610 for acquiring an image comprising a plurality of faces;
a selecting module 620, configured to select at least one face image from the images of the plurality of faces;
a comparison module 630, configured to compare the at least one facial image with a facial image of the target user;
and a determining module 640, configured to determine whether the identification is successful based on the comparison result.
According to the embodiment of the application, the images comprising the faces are collected, at least one face image is selected from the images comprising the faces, the face images are compared with the face image of the target user based on the at least one face image, whether the face images are successfully identified is determined based on the comparison result, whether the face images which can be successfully identified exist in the images comprising the faces is identified, the success rate of user identity authentication and face payment by adopting the face images is ensured, the follow-up user can be ensured to smoothly complete the whole face payment process, and the full link passing rate is improved.
The scheme of the embodiment of the application also helps the user to learn the process of using the face payment, so that the user can experience the intelligence of the face payment and is favorable for popularization of the face payment due to unique user experience.
In one embodiment, the acquisition module 610 includes:
the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring a plurality of faces simultaneously so as to acquire images comprising the faces.
In one embodiment, the comparison module 630 comprises:
and the sending unit is used for sending the identification of the target user and the characteristic information of the at least one face image to a server so as to compare the face characteristic information of the target user stored on the server through the server.
In one embodiment, the selection module 620 includes:
and the selecting unit is used for selecting the face image with the largest size if the face with the largest size exists in the images of the faces.
In one embodiment, the terminal device 600 further includes:
the executing module 650 is configured to execute a reminding operation if at least two face images exist in the images of the faces and a difference between sizes of two face images with a largest size in the at least two face images is smaller than a predetermined threshold.
Fig. 7 is a schematic structural diagram of a terminal device provided in an embodiment of the present specification. Referring to fig. 7, in the hardware level, the terminal device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the terminal device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 7, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form the association device of the resource value-added object and the resource object on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring a face image to be recognized;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image.
According to the method and the device, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of face image selection of the user is improved.
The method for assisting in face recognition disclosed in the embodiment of fig. 1 in this specification may be applied to a processor, or may be implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in one or more embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in hardware, in a software module executed by a hardware decoding processor, or in a combination of the hardware and software modules executed by a hardware decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The terminal device may further execute an auxiliary method for face recognition executed by the auxiliary system for face recognition in fig. 7, which is not described herein again.
Of course, the terminal device in this specification does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, besides the software implementation, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Fig. 8 is a schematic structural diagram of a terminal device provided in an embodiment of the present specification. Referring to fig. 8, in the hardware level, the terminal device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the terminal device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 8, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form the association device of the resource value-added object and the resource object on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
According to the embodiment of the application, the images comprising the faces are collected, at least one face image is selected from the images comprising the faces, the face images are compared with the face image of the target user based on the at least one face image, whether the face images are successfully identified is determined based on the comparison result, whether the face images which can be successfully identified exist in the images comprising the faces is identified, the success rate of user identity authentication and face payment by adopting the face images is ensured, the follow-up user can be ensured to smoothly complete the whole face payment process, and the full link passing rate is improved. Meanwhile, the method is a process for helping the user to learn to use the face payment, so that the user can feel the intelligence of the face payment and is favorable for popularization of the face payment due to unique user experience.
The face recognition method disclosed in the embodiment shown in fig. 4 of the present specification can be applied to a processor, or can be implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in one or more embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in hardware, in a software module executed by a hardware decoding processor, or in a combination of the hardware and software modules executed by a hardware decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The terminal device may further execute a face recognition method executed by the face recognition system of fig. 8, which is not described herein again.
Of course, the terminal device in this specification does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, besides the software implementation, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the above method embodiments, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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 a system 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 an instruction system 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 identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (17)
1. An auxiliary method for face recognition is applied to terminal equipment, and comprises the following steps:
acquiring a face image to be recognized comprising a plurality of faces;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image, wherein the face distribution abnormality comprises that at least two faces with similar sizes exist in the face image, and the sizes of the at least two faces are the ratio of the size of a corresponding face detection frame to the distance from the center of the face detection frame to the center of a screen.
2. The method of claim 1, obtaining a face distribution in the face image, comprising:
taking the face image as the input of a plurality of face detection models to obtain the output face distribution;
the multi-face detection model is obtained by training based on face image samples with face distribution abnormity.
3. The method according to claim 1 or 2,
the face distribution abnormality further includes: at least one of the face image does not have a face, the face image has at least two faces, and the feature information of the face in the face image does not meet the preset conditions.
4. The method of claim 3, wherein if the face distribution abnormality includes that no face exists in the face image, performing a reminding operation corresponding to the face distribution, including:
and reminding the user that the user is located in an image acquisition area of an image acquisition device of the terminal equipment based on the fact that the face does not exist in the face image.
5. The method of claim 1, wherein if the face distribution abnormality includes at least two faces with similar sizes existing in the face image, performing a reminding operation corresponding to the face distribution, including:
and if the difference between the sizes of the two faces with the largest size in the at least two faces is smaller than a preset threshold value, reminding the user to ensure that other users do not exist in the acquisition area of the image acquisition device of the terminal equipment.
6. The method of claim 3, wherein if the face distribution abnormality includes at least two faces existing in the face image, performing a reminding operation corresponding to the face distribution, including:
selecting a face with the largest size in the face image;
determining whether the characteristic information of the face with the largest size meets face recognition comparison conditions;
and if not, reminding the user to keep a preset distance from the image acquisition device of the terminal equipment.
7. A face recognition method, comprising:
acquiring an image comprising a plurality of faces;
if at least two faces with similar sizes exist in the images of the faces, executing a reminding operation, wherein the sizes of the at least two faces are the ratio of the sizes of the corresponding face detection frames to the distance from the center of the face detection frame to the center of the screen;
selecting at least one face image from the images of the faces based on the size of each face in the images;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
8. The method of claim 7, the acquiring an image comprising a plurality of human faces, comprising:
a plurality of faces are simultaneously acquired to obtain an image including the plurality of faces.
9. The method of claim 7, the comparing based on the at least one facial image and a facial image of a target user, comprising:
and sending the identification of the target user and the characteristic information of the at least one face image to a server so as to compare the face characteristic information of the target user stored on the server through the server.
10. The method of claim 7, the selecting at least one face image from the images of the plurality of faces comprising:
and if the face with the largest size exists in the images of the faces, selecting the face image with the largest size.
11. The method of claim 7, wherein if at least two faces with similar sizes exist in the images of the faces, performing a reminding operation, comprising:
and if at least two faces with similar sizes exist in the images of the faces, and the size difference of two faces with the largest size in the at least two faces is smaller than a preset threshold value, executing a reminding operation.
12. A terminal device, comprising:
the system comprises a first acquisition module, a second acquisition module and a recognition module, wherein the first acquisition module is used for acquiring a face image to be recognized, which comprises a plurality of faces;
the second acquisition module is used for acquiring the face distribution in the face image;
and the execution module is used for executing a reminding operation corresponding to the face distribution based on the face distribution in the face image when the face distribution is abnormal, wherein the face distribution abnormality comprises that at least two faces with similar sizes exist in the face image, and the sizes of the at least two faces are the ratio of the size of the corresponding face detection frame to the distance from the center of the face detection frame to the center of the screen.
13. A terminal device, comprising:
the system comprises an acquisition module, a display module and a processing module, wherein the acquisition module is used for acquiring images comprising a plurality of human faces;
the reminding module is used for executing reminding operation if at least two faces with similar sizes exist in the images of the faces, wherein the sizes of the at least two faces are the ratio of the sizes of the corresponding face detection frames to the distance from the center of the face detection frame to the center of the screen;
a selection module for selecting at least one face image from the images of the plurality of faces based on the size of each face in the images;
the comparison module is used for comparing the at least one face image with the face image of the target user;
and the determining module is used for determining whether the identification is successful or not based on the comparison result.
14. A terminal device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:
acquiring a face image to be recognized comprising a plurality of faces;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image, wherein the face distribution abnormality comprises that at least two faces with similar sizes exist in the face image, and the sizes of the at least two faces are the ratio of the size of a corresponding face detection frame to the distance from the center of the face detection frame to the center of a screen.
15. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a face image to be recognized comprising a plurality of faces;
acquiring the face distribution in the face image;
and when the face distribution is abnormal, executing a reminding operation corresponding to the face distribution based on the face distribution in the face image, wherein the face distribution abnormality comprises that at least two faces with similar sizes exist in the face image, and the sizes of the at least two faces are the ratio of the size of a corresponding face detection frame to the distance from the center of the face detection frame to the center of a screen.
16. A terminal device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:
acquiring an image comprising a plurality of faces;
if at least two faces with similar sizes exist in the images of the faces, executing a reminding operation, wherein the sizes of the at least two faces are the ratio of the sizes of the corresponding face detection frames to the distance from the center of the face detection frame to the center of the screen;
selecting at least one face image from the images of the faces based on the size of each face in the images;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
17. A computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring an image comprising a plurality of faces;
if at least two faces with similar sizes exist in the images of the faces, executing a reminding operation, wherein the sizes of the at least two faces are the ratio of the sizes of the corresponding face detection frames to the distance from the center of the face detection frame to the center of the screen;
selecting at least one face image from the images of the faces based on the size of each face in the images;
comparing the at least one face image with a face image of a target user;
whether the recognition is successful is determined based on the comparison result.
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