CN111241930A - Method and system for face recognition - Google Patents
Method and system for face recognition Download PDFInfo
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- 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
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- G06Q20/00—Payment architectures, schemes or protocols
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- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
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- G06Q20/40145—Biometric identity checks
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- 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
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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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Abstract
The invention discloses a method and a system for face recognition, and belongs to the technical field of image recognition. The method comprises the following steps: acquiring a face image of a target crowd, acquiring face image characteristic data of the target crowd according to all face images of the target crowd, and storing the face image characteristic data of the target crowd in a database; determining an identification area, acquiring a target face image after a target enters the identification area, sending a face identification request, and entering a request queue for queuing; after queuing is finished, reading a target face image, cutting the target face image, and drawing a face frame to obtain a face image to be detected; extracting the human face characteristic points of the human face image to be detected to obtain characteristic points; and comparing the matching data with a preset threshold value, and performing face recognition. The invention improves the efficiency, the accurate removing rate and the safety of face recognition, and can be applied to various life scenes, including: real-name authentication, face-brushing payment, tax payment authentication, video monitoring and the like.
Description
Technical Field
The present invention relates to the field of image recognition technology, and more particularly, to a method and system for face recognition.
Background
In recent years, the face recognition technology is more and more advanced, from the initial pattern recognition to the current machine learning and deep learning, the accuracy of face recognition is more and more improved, the required scenes are more and more, the requirements of face brushing payment, registration handling, trip and the like are included, the application of face brushing is more and more extensive, and the characteristics of convenience and quickness are deeply welcomed by people.
Disclosure of Invention
The invention aims to utilize a more efficient face recognition result to be more quickly and conveniently applied to various scenes, and provides a method for face recognition, which comprises the following steps:
acquiring a face image of a target population, checking the face image, removing the face image which does not meet the standard, and acquiring again until all face images of the target population are acquired, acquiring face image characteristic data of the target population according to all the face images of the target population, and storing the face image characteristic data of the target population into a database;
determining an identification area, acquiring a target face image after a target enters the identification area, sending a face identification request, and entering a request queue for queuing;
after queuing is finished, reading a target face image, cutting the target face image, and drawing a face frame to obtain a face image to be detected;
extracting the human face characteristic points of the human face image to be detected to obtain characteristic points;
and searching the database according to the feature points, determining feature data matched with the feature points, matching the feature data with the feature points to obtain matched data, comparing the matched data with a preset threshold value, and performing face recognition.
Optionally, the method further comprises:
if the matching data is larger than a preset threshold value, determining that the target face can be identified and belongs to the target crowd;
and if the matching data is smaller than the preset threshold value, determining that the target face cannot be identified and does not belong to the target group.
Optionally, the method further comprises:
and after the target face image is collected, determining whether the target face image is in compliance, and if not, carrying out secondary collection.
Optionally, matching is performed in a way that feature points are 1: 1 or 1: and traversing the characteristic data in the mode of N, and matching.
Optionally, the face image that does not meet the specification is a low resolution or incomplete face in the image.
The invention also provides a system for face recognition, comprising:
the acquisition module is used for acquiring the face images of the target population, checking the face images, eliminating the face images which do not meet the standard, acquiring the face images again until all the face images of the target population are acquired, acquiring the face image characteristic data of the target population according to all the face images of the target population, and storing the face image characteristic data of the target population into the database;
the request identification module is used for determining an identification area, acquiring a target face image after a target enters the identification area, sending a face identification request, and entering a request queue for queuing;
the processing module is used for reading the target face image after queuing is finished, cutting the target face image and drawing a face frame to obtain a face image to be detected;
the characteristic point extraction module is used for extracting the human face characteristic points of the human face image to be detected to obtain characteristic points;
and the recognition module is used for searching the database according to the feature points, determining feature data matched with the feature points, matching the feature data with the feature points, acquiring matched data, comparing the matched data with a preset threshold value and recognizing the face.
Optionally, the identification module is further configured to:
determining that the target face can be identified and belongs to the target crowd when the matching data is larger than a preset threshold;
and determining that the target face cannot be identified and does not belong to the target crowd when the matching data is smaller than a preset threshold value.
Optionally, the request identifying module is further configured to:
and after the target face image is collected, determining whether the target face image is in compliance, and if not, carrying out secondary collection.
Optionally, matching is performed in a way that feature points are 1: 1 or 1: and traversing the characteristic data in the mode of N, and matching.
Optionally, the face image that does not meet the specification is a low resolution or incomplete face in the image.
The invention improves the efficiency, the accurate removing rate and the safety of face recognition, and can be applied to various life scenes, including: real-name authentication, face-brushing payment, tax payment authentication, video monitoring and the like.
Drawings
FIG. 1 is a flow chart of a method for face recognition according to the present invention;
FIG. 2 is a flowchart of an embodiment of a method for face recognition according to the present invention;
fig. 3 is a block diagram of a system for face recognition according to the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The invention provides a method for face recognition, as shown in fig. 1, comprising:
acquiring a face image of a target population, checking the face image, removing the face image which does not meet the standard, and acquiring again until all face images of the target population are acquired, acquiring face image characteristic data of the target population according to all the face images of the target population, and storing the face image characteristic data of the target population into a database;
determining an identification area, acquiring a target face image after a target enters the identification area, sending a face identification request, and entering a request queue for queuing;
and after the target face image is collected, determining whether the target face image is in compliance, and if not, carrying out secondary collection.
After queuing is finished, reading a target face image, cutting the target face image, and drawing a face frame to obtain a face image to be detected;
extracting the human face characteristic points of the human face image to be detected to obtain characteristic points;
and searching the database according to the feature points, determining feature data matched with the feature points, matching the feature data with the feature points to obtain matched data, comparing the matched data with a preset threshold value, and performing face recognition.
If the matching data is larger than a preset threshold value, determining that the target face can be identified and belongs to the target crowd;
and if the matching data is smaller than the preset threshold value, determining that the target face cannot be identified and does not belong to the target group.
Matching is performed by taking characteristic points as 1: 1 or 1: and traversing the characteristic data in the mode of N, and matching.
The face image that does not meet the specification is low in resolution or incomplete in face.
The invention will now be further illustrated with reference to the following examples, as shown in FIG. 2.
Step 1, training a model by using an insight face, loading a pre-training model firstly, aiming at ensuring a weight parameter to be at a high-efficiency level, preprocessing an input picture by mtcnn and cutting the preprocessed picture into a picture size required by model training, and finishing the training of the model through a loss function.
And 2, the client sends a face recognition request to the server under various concurrent conditions, and sends the picture to be recognized to the server as request data.
And 3, the server side places the large concurrent requests into a request queue by using a tornado and flash frame, so as to avoid the risk of hanging up the large concurrent system.
And 4, saving the data sent by the client into a picture and reading the picture into mtcnn.
And step 5, processing the picture through mtcnn, detecting the face, returning a result to execute the step 10 if no face exists, cutting the picture and drawing a frame of the face under the condition of the face, and providing conditions for extracting the feature points behind.
And 6, loading the model trained by the instightface, extracting the human face characteristic points of the picture cut in the step 5 through loading the model, and correcting the vector matrix corresponding to the characteristic points.
And 7, searching and comparing the matrix returned in the step 6 with the picture data stored in the database.
And 8, judging the Euclidean distance between the distribution calculation step 6 and the picture searched in the database and a preset threshold value.
And 9, if the description is the same person, executing step 10, and returning a result, if the description is not the same person, executing step 10, and returning a result.
The present invention also provides a system 200 for face recognition, as shown in fig. 3, including:
the acquisition module 201 is used for acquiring the face images of the target population, checking the face images, eliminating the face images which do not meet the standard, acquiring the face images again until all the face images of the target population are acquired, acquiring the face image feature data of the target population according to all the face images of the target population, and storing the face image feature data of the target population into a database;
the request identification module 202 determines an identification area, acquires a target face image after a target enters the identification area, sends a face identification request, and enters a request queue for queuing;
the request identification module 202 is further configured to:
and after the target face image is collected, determining whether the target face image is in compliance, and if not, carrying out secondary collection.
The processing module 203 reads the target face image after the queuing is finished, cuts the target face image and draws a face frame to obtain a face image to be detected;
the feature point extraction module 204 is used for extracting the human face feature points of the human face image to be detected to obtain feature points;
the recognition module 205 is configured to retrieve the database according to the feature points, determine feature data matched with the feature points, match the feature data with the feature points, obtain matching data, compare the matching data with a preset threshold, and perform face recognition;
determining that the target face can be identified and belongs to the target crowd when the matching data is larger than a preset threshold;
and determining that the target face cannot be identified and does not belong to the target crowd when the matching data is smaller than a preset threshold value.
Matching is performed by taking characteristic points as 1: 1 or 1: and traversing the characteristic data in the mode of N, and matching.
The face image that does not meet the specification is low in resolution or incomplete in face.
The invention improves the efficiency, the accurate removing rate and the safety of face recognition, and can be applied to various life scenes, including: real-name authentication, face-brushing payment, tax payment authentication, video monitoring and the like.
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 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for face recognition, the method comprising:
acquiring a face image of a target population, checking the face image, removing the face image which does not meet the standard, and acquiring again until all face images of the target population are acquired, acquiring face image characteristic data of the target population according to all the face images of the target population, and storing the face image characteristic data of the target population into a database;
determining an identification area, acquiring a target face image after a target enters the identification area, sending a face identification request, and entering a request queue for queuing;
after queuing is finished, reading a target face image, cutting the target face image, and drawing a face frame to obtain a face image to be detected;
extracting the human face characteristic points of the human face image to be detected to obtain characteristic points;
and searching the database according to the feature points, determining feature data matched with the feature points, matching the feature data with the feature points to obtain matched data, comparing the matched data with a preset threshold value, and performing face recognition.
2. The method of claim 1, further comprising:
if the matching data is larger than a preset threshold value, determining that the target face can be identified and belongs to the target crowd;
and if the matching data is smaller than the preset threshold value, determining that the target face cannot be identified and does not belong to the target group.
3. The method of claim 1, further comprising:
and after the target face image is collected, determining whether the target face image is in compliance, and if not, carrying out secondary collection.
4. The method of claim 1, the matching being a ratio of feature points in a 1: 1 or 1: and traversing the characteristic data in the mode of N, and matching.
5. The method of claim 1, wherein the non-compliant face image is a low resolution or incomplete face image.
6. A system for face recognition, the system comprising:
the acquisition module is used for acquiring the face images of the target population, checking the face images, eliminating the face images which do not meet the standard, acquiring the face images again until all the face images of the target population are acquired, acquiring the face image characteristic data of the target population according to all the face images of the target population, and storing the face image characteristic data of the target population into the database;
the request identification module is used for determining an identification area, acquiring a target face image after a target enters the identification area, sending a face identification request, and entering a request queue for queuing;
the processing module is used for reading the target face image after queuing is finished, cutting the target face image and drawing a face frame to obtain a face image to be detected;
the characteristic point extraction module is used for extracting the human face characteristic points of the human face image to be detected to obtain characteristic points;
and the recognition module is used for searching the database according to the feature points, determining feature data matched with the feature points, matching the feature data with the feature points, acquiring matched data, comparing the matched data with a preset threshold value and recognizing the face.
7. The system of claim 6, the identification module further to:
determining that the target face can be identified and belongs to the target crowd when the matching data is larger than a preset threshold;
and determining that the target face cannot be identified and does not belong to the target crowd when the matching data is smaller than a preset threshold value.
8. The system of claim 6, the request identification module further to:
and after the target face image is collected, determining whether the target face image is in compliance, and if not, carrying out secondary collection.
9. The system of claim 6, the matching being a ratio of feature points in a 1: 1 or 1: and traversing the characteristic data in the mode of N, and matching.
10. The system of claim 6, wherein the non-compliant face image is a low resolution or incomplete face image.
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| CN201911390559.9A CN111241930A (en) | 2019-12-30 | 2019-12-30 | Method and system for face recognition |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111985425A (en) * | 2020-08-27 | 2020-11-24 | 闽江学院 | Image verification device under multi-person scene |
| CN112001334A (en) * | 2020-08-27 | 2020-11-27 | 闽江学院 | Portrait recognition device |
| CN112101200A (en) * | 2020-09-15 | 2020-12-18 | 北京中合万象科技有限公司 | Human face anti-recognition method, system, computer equipment and readable storage medium |
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