CN112395965A - Mobile terminal face recognition system and method based on power intranet - Google Patents
Mobile terminal face recognition system and method based on power intranet Download PDFInfo
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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- G06V40/168—Feature extraction; Face representation
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
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Abstract
The invention relates to a mobile terminal face recognition system and a method based on an electric power intranet, wherein the system comprises a living body detection module, a face recognition module and a face recognition module, wherein the living body detection module is used for carrying out living body detection on a user so as to verify whether the user is a real living body; the face information acquisition module is used for acquiring a front photo image of a face of a user; the face contour cutting module is used for processing the collected face image photos, cutting according to an effective face contour area, and performing transparentization processing on an area outside the face contour area; the face recognition module is used for comparing the cut face image photo with the face photo data stored in the artificial intelligence platform database; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails.
Description
Technical Field
The invention belongs to the technical field of power equipment, and particularly relates to a mobile terminal face recognition system and method based on a power intranet.
Background
With the rapid development of the mobile internet, the mobile application of the smart phone is explosively increased in various industries, and various user interaction modes are diversified.
In the field mobile operation working scene of the power industry, field operation personnel use the mobile terminal, and the identity verification scene is very common.
The mode of account number + password is adopted to carry out identity authentication in the traditional mode, the password rule comprises upper and lower case letters, numbers, special characters and the like, and a user often forgets the password and is inconvenient to enter the password. Some users set weak passwords for convenient login, have serious potential safety hazards and are very easy to crack by violence. Under the complex password rule, the time from entering the account password to passing the login verification is too long, and the efficiency is low. This is a disadvantage of the prior art.
In view of the above, the invention provides a mobile terminal face recognition system and method based on an electric power intranet; it is very necessary to solve the defects existing in the prior art.
Disclosure of Invention
The present invention is directed to provide a system and a method for recognizing a face of a mobile terminal based on an intranet, so as to solve the above technical problems.
In order to achieve the purpose, the invention provides the following technical scheme:
a mobile terminal face recognition system based on electric power intranet includes:
the living body detection module is used for carrying out living body detection on the user so as to verify whether the user is a real living body; by verifying whether the user operates in a real living body, common attack means such as photos, face changing, masks, sheltering and screen copying are effectively resisted, so that the user is helped to discriminate fraudulent behaviors, and the benefit of the user is guaranteed.
The face information acquisition module is used for acquiring a front photo image of a face of a user;
the face contour cutting module is used for processing the collected face image photos, cutting according to an effective face contour area, and performing transparentization processing on an area outside the face contour area;
the face recognition module is used for comparing the cut face image photo with the face photo data stored in the artificial intelligence platform database; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails.
Preferably, in the living body detection module, the living body detection is performed on the user through a living body detection algorithm, and the specific method is as follows:
extracting fusion texture features of the human face by adopting an adjacent local binary pattern and a local gradient pattern, wherein the fusion texture features comprise color feature information, spatial feature information, gradient feature information and texture feature information; and carrying out local image recognition on the acquired information, and if the acquired information meets the threshold value condition, determining that the information is the living body.
Preferably, in the face information acquisition module, a video streaming mode is adopted to acquire a front photo image of a face of a user; one or more face front photos are collected for image recognition, the face photos are collected in real time in a video streaming mode, and collected non-front face images such as nodding heads, shaking heads and the like are avoided.
Preferably, in the face recognition module, the cut face image photo is compared with the face photo data stored in the artificial intelligence platform database, and the image in the searching process adopts a method of 1: and 1, performing face search.
The invention also provides a mobile terminal face recognition method based on the power intranet, which comprises the following steps:
s1: a step of in vivo detection, which is to perform in vivo detection on the user to verify whether the user is the real in vivo person; by verifying whether the user operates in a real living body, common attack means such as photos, face changing, masks, sheltering and screen copying are effectively resisted, so that the user is helped to discriminate fraudulent behaviors, and the benefit of the user is guaranteed.
S2: a step of acquiring face information, namely acquiring a front photo image of a face of a user;
s3: the step of cutting the face contour, which is to process the collected face image photo, cut the face image photo according to the effective face contour area, and perform transparentization treatment on the area outside the face contour area;
s4: a step of face recognition, which is to compare the cut face image photo with the face photo data stored in the database of the artificial intelligent platform; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails.
Preferably, in step S1, the live body detection is performed on the user by a live body detection algorithm, which includes:
extracting fusion texture features of the human face by adopting an adjacent local binary pattern and a local gradient pattern, wherein the fusion texture features comprise color feature information, spatial feature information, gradient feature information and texture feature information; and carrying out local image recognition on the acquired information, and if the acquired information meets the threshold value condition, determining that the information is the living body.
Preferably, in step S2, a video stream mode is adopted to collect a front photo image of a face of the user; one or more face front photos are collected for image recognition, the face photos are collected in real time in a video streaming mode, and collected non-front face images such as nodding heads, shaking heads and the like are avoided.
Preferably, in step S4, the clipped human face image photo is compared with the human face image data stored in the artificial intelligence platform database, and the image in the searching process adopts a 1: and 1, performing face search.
The method has the advantages that the business logic is packaged into the mobile terminal face recognition public component, the recognition safety is improved through the action living body detection, each link from the face recognition to the unified authority verification is simplified based on the standard face recognition interface packaging, the integration complexity of the mobile application is reduced, and the research and development efficiency is improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
Fig. 1 is a schematic block diagram of a mobile terminal face recognition system based on an electric power intranet according to the present invention.
Fig. 2 is a flowchart of a mobile terminal face recognition method based on an electric power intranet according to the present invention.
The system comprises a living body detection module 1, a human face information acquisition module 2, a facial contour cutting module 3 and a human face recognition module 4.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings by way of specific examples, which are illustrative of the present invention and are not limited to the following embodiments.
Example 1:
as shown in fig. 1, the mobile terminal face recognition system based on the power intranet according to the present embodiment includes:
the living body detection module 1 is used for carrying out living body detection on the user so as to verify whether the user is a real living body; by verifying whether the user operates in a real living body, common attack means such as photos, face changing, masks, sheltering and screen copying are effectively resisted, so that the user is helped to discriminate fraudulent behaviors, and the benefit of the user is guaranteed. The method for detecting the living body of the user through the living body detection algorithm comprises the following steps:
extracting fusion texture features of the human face by adopting an adjacent local binary pattern and a local gradient pattern, wherein the fusion texture features comprise color feature information, spatial feature information, gradient feature information and texture feature information; and carrying out local image recognition on the acquired information, and if the acquired information meets the threshold value condition, determining that the information is the living body.
The face information acquisition module 2 is used for acquiring a front photo image of a face of a user; acquiring a front photo image of a face of a user in a video streaming mode; one or more face front photos are collected for image recognition, the face photos are collected in real time in a video streaming mode, and collected non-front face images such as nodding heads, shaking heads and the like are avoided.
The face contour cutting module 3 is used for processing the collected face image photos, cutting the face image photos according to an effective face contour area, and performing transparentization processing on the area outside the face contour area;
the face recognition module 4 is used for comparing the cut face image photo with the face photo data stored in the artificial intelligence platform database; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails. And comparing the cut human face image photo with human face photo data stored in an artificial intelligence platform database, wherein the image is obtained by the following steps: and 1, performing face search.
Example 2:
as shown in fig. 2, the embodiment further provides a method for recognizing a face of a mobile terminal based on an electric power intranet, which includes the following steps:
s1: a step of in vivo detection, which is to perform in vivo detection on the user to verify whether the user is the real in vivo person; by verifying whether the user operates in a real living body, common attack means such as photos, face changing, masks, sheltering and screen copying are effectively resisted, so that the user is helped to discriminate fraudulent behaviors, and the benefit of the user is guaranteed. The method for detecting the living body of the user through the living body detection algorithm comprises the following steps:
extracting fusion texture features of the human face by adopting an adjacent local binary pattern and a local gradient pattern, wherein the fusion texture features comprise color feature information, spatial feature information, gradient feature information and texture feature information; and carrying out local image recognition on the acquired information, and if the acquired information meets the threshold value condition, determining that the information is the living body.
S2: a step of acquiring face information, namely acquiring a front photo image of a face of a user; acquiring a front photo image of a face of a user in a video streaming mode; one or more face front photos are collected for image recognition, the face photos are collected in real time in a video streaming mode, and collected non-front face images such as nodding heads, shaking heads and the like are avoided.
S3: the step of cutting the face contour, which is to process the collected face image photo, cut the face image photo according to the effective face contour area, and perform transparentization treatment on the area outside the face contour area;
s4: a step of face recognition, which is to compare the cut face image photo with the face photo data stored in the database of the artificial intelligent platform; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails. And comparing the cut human face image photo with human face photo data stored in an artificial intelligence platform database, wherein the image is obtained by the following steps: and 1, performing face search.
The above disclosure is only for the preferred embodiments of the present invention, but the present invention is not limited thereto, and any non-inventive changes that can be made by those skilled in the art and several modifications and amendments made without departing from the principle of the present invention shall fall within the protection scope of the present invention.
Claims (8)
1. The utility model provides a mobile terminal face identification system based on electric power intranet which characterized in that includes:
the living body detection module is used for carrying out living body detection on the user so as to verify whether the user is a real living body;
the face information acquisition module is used for acquiring a front photo image of a face of a user;
the face contour cutting module is used for processing the collected face image photos, cutting according to an effective face contour area, and performing transparentization processing on an area outside the face contour area;
the face recognition module is used for comparing the cut face image photo with the face photo data stored in the artificial intelligence platform database; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails.
2. The system according to claim 1, wherein the in-vivo detection module performs in-vivo detection on the user through an in-vivo detection algorithm, and the specific method is as follows:
extracting fusion texture features of the human face by adopting an adjacent local binary pattern and a local gradient pattern, wherein the fusion texture features comprise color feature information, spatial feature information, gradient feature information and texture feature information; and carrying out local image recognition on the acquired information, and if the acquired information meets the threshold value condition, determining that the information is the living body.
3. The system according to claim 2, wherein the face information collection module collects a photo image of a face of the user in a video stream mode.
4. The system according to claim 3, wherein the face recognition module performs image comparison between the cut face image photo and the face photo data stored in the database of the artificial intelligence platform, and the image in the search process is 1: and 1, performing face search.
5. A face recognition method of a mobile terminal based on an electric power intranet is characterized by comprising the following steps:
s1: a step of in vivo detection, which is to perform in vivo detection on the user to verify whether the user is the real in vivo person;
s2: a step of acquiring face information, namely acquiring a front photo image of a face of a user;
s3: the step of cutting the face contour, which is to process the collected face image photo, cut the face image photo according to the effective face contour area, and perform transparentization treatment on the area outside the face contour area;
s4: a step of face recognition, which is to compare the cut face image photo with the face photo data stored in the database of the artificial intelligent platform; comparing the two, if the similarity meets the preset threshold, the verification is successful; otherwise, the verification fails.
6. The method for recognizing a face of a mobile terminal based on an electric power intranet according to claim 5, wherein in the step S1, the living body of the user is detected by a living body detection algorithm, and the specific method is as follows:
extracting fusion texture features of the human face by adopting an adjacent local binary pattern and a local gradient pattern, wherein the fusion texture features comprise color feature information, spatial feature information, gradient feature information and texture feature information; and carrying out local image recognition on the acquired information, and if the acquired information meets the threshold value condition, determining that the information is the living body.
7. The method for recognizing a face of a mobile terminal based on an electric power intranet according to claim 6, wherein in step S2, a video stream mode is adopted to collect a photo image of a face of a user.
8. The method according to claim 7, wherein in step S4, the cut facial image is compared with facial image data stored in the database of the artificial intelligence platform, and the image in the search process is 1: and 1, performing face search.
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Application publication date: 20210223 |