CN110443577A - A kind of campus attendance checking system based on recognition of face - Google Patents
A kind of campus attendance checking system based on recognition of face Download PDFInfo
- Publication number
- CN110443577A CN110443577A CN201910701123.0A CN201910701123A CN110443577A CN 110443577 A CN110443577 A CN 110443577A CN 201910701123 A CN201910701123 A CN 201910701123A CN 110443577 A CN110443577 A CN 110443577A
- Authority
- CN
- China
- Prior art keywords
- face
- feature point
- recognition
- server
- student
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000001815 facial effect Effects 0.000 claims abstract description 8
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 14
- 239000000284 extract Substances 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 239000013598 vector Substances 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000036544 posture Effects 0.000 description 2
- 241001469893 Oxyzygonectes dovii Species 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000005284 basis set Methods 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 230000008921 facial expression Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Oral & Maxillofacial Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Multimedia (AREA)
- Human Computer Interaction (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Collating Specific Patterns (AREA)
Abstract
The present invention provides a kind of campus attendance checking system based on recognition of face, is related to field of face identification, which includes: image acquisition device, identification terminal, server；Multiple described facial images are sent to the identification terminal for acquiring multiple facial images of student to be identified by described image collector；The identification terminal carries out feature point extraction for multiple facial images based on the received, to obtain notable feature point set, and the notable feature point set is sent to server；The student information to obtain student information corresponding with the notable feature point set, and is returned to the identification terminal for matching according to the face characteristic point data base prestored to the notable feature point set by the server；Wherein, the set of characteristic points of student of the face characteristic point data base for storing current school and corresponding student information can reduce the error rate of recognition of face and improve the speed of recognition of face.
Description
Technical Field
The invention relates to the field of face recognition, in particular to a campus attendance system based on face recognition.
Background
At present, two modes of NFC card swiping or fingerprint inputting are mainly and generally adopted for campus attendance, and the two modes have a plurality of defects:
(1) NFC card is lost easily, and the student mends the card frequently, causes unnecessary waste.
(2) The fingerprint can not be recorded under certain conditions (such as weather heat, abnormal fingerprint and the like), so that the attendance cannot be normally finished.
With the continuous development of artificial intelligence technology, image processing technology and other technologies, face recognition has been widely applied to various fields of social activities. For example: face recognition intelligent unlocking, face recognition intelligent payment, face recognition intelligent security inspection and the like. The face recognition is widely applied to attendance checking, but the traditional face recognition is applied to the campus attendance checking at present and has the following defects:
(1) the facial features of students are weak, and the recognition error rate is large.
(2) The number of school people is large, the speed is low through the identification mode of the server, and the requirement of normal attendance speed cannot be met.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a campus attendance system based on face recognition, which can realize fast and accurate face recognition and meet the campus attendance use requirements.
The invention provides a campus attendance system based on face recognition, which comprises:
the system comprises an image collector, an identification terminal and a server;
the image collector is used for collecting a plurality of face images of a student to be identified and sending the face images to the identification terminal;
the recognition terminal is used for extracting feature points according to the received multiple face images to obtain a salient feature point set and sending the salient feature point set to the server;
the server is used for matching the salient feature point set according to a prestored face feature point database to obtain student information corresponding to the salient feature point set and returning the student information to the identification terminal; the face feature point database is used for storing feature point sets of students of the current school and corresponding student information.
Optionally, the identification terminal is specifically configured to use an Eigenface algorithm to extract feature points of each face image from the plurality of face images respectively; and denoising the extracted feature points of each human face image, stripping the feature points with obvious abnormality, and fusing the feature points of a plurality of human faces by an average distance method to generate a set of obvious feature points of the students to be recognized.
Optionally, the set of salient feature points has 1024 feature point dimensions, and the 1024 feature points comprise the best set of facial feature point identifications.
Optionally, the identification terminal further comprises a fingerprint collector and a loudspeaker;
when the acquired student information is multiple, the identification terminal is further used for starting a loudspeaker to prompt the student to input the fingerprint information of the student through the fingerprint collector and sending the collected fingerprint information to the server; the server is further used for selecting corresponding student information from the acquired student information according to the fingerprint information and sending the student information to the identification terminal.
Optionally, when the obtained student information is multiple pieces, and when the obtained student information is multiple pieces, the server is further configured to extract an eye print feature corresponding to an eye region of a human face from the human face image, input the eye print feature into a pre-established neural network to obtain an identification result of the neural network for the eye print feature, and send the student information corresponding to the identification result to the identification terminal.
Optionally, the server is further configured to update, at predetermined intervals, information of a feature point set corresponding to the student in the face database according to the significant feature point set.
Optionally, the identification terminal is configured to perform feature point extraction on the faces of the students, extract at least 10 avatar feature points, and normalize the extracted feature points into a 1024-dimensional feature point set by using an average distance method.
Optionally, the server is specifically configured to load feature points of all the avatars of the school in the memory in advance, perform matching through an algorithm of a minimum distance between the feature points, and return a final result to the identification terminal.
Compared with the prior art, the system provided by the embodiment of the invention has the following advantages: the embodiment of the invention comprises an image collector, an identification terminal and a server, wherein the image collector collects a plurality of face images of students to be identified, sends the plurality of face images to the identification terminal, extracts feature points according to the received plurality of face images to obtain a significant feature point set, sends the significant feature point set to the server, matches the significant feature point set according to a prestored face feature point database to obtain student information corresponding to the significant feature point set, and returns the student information to the identification terminal to complete face identification, so that the error rate of face identification can be reduced, and the identification speed can be improved.
In addition to the technical problems solved by the embodiments of the present invention, the technical features constituting the technical solutions, and the advantages brought by the technical features of the technical solutions described above, other technical problems that can be solved by the face recognition system provided by the embodiments of the present invention, other technical features included in the technical solutions, and advantages brought by the technical features will be further described in detail in the detailed description.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a campus attendance system based on face recognition according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an identification terminal according to another embodiment of the present invention;
fig. 3 is a schematic flow chart of a campus attendance system based on face recognition according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of an identification terminal according to another embodiment of the present invention;
fig. 5 is a functional block diagram of a campus attendance system based on face recognition according to an embodiment of the present invention.
A description of the reference numerals;
the system comprises an image collector 1, an identification terminal 2, a server 3, a loudspeaker 4 and a fingerprint collector 5.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Face recognition: the method is based on a face image, a plurality of face images are collected through an image collector and then sent to a recognition terminal, the recognition terminal extracts feature points of the collected face images to obtain a significant feature point set, trains a face model and the like, and then the extracted feature point set is matched with images prestored in a server to realize a face recognition technology.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a campus attendance system based on face recognition according to an embodiment of the present invention. In this embodiment, the campus attendance system based on face recognition specifically includes:
the system comprises an image collector 1, an identification terminal 2 and a server 3; wherein,
the image collector 1 is used for collecting a plurality of face images of a student to be identified and sending the face images to the identification terminal 2.
In this embodiment, the image collector 1 may be a camera, which is fixed to the recognition terminal 2 and is configured to collect face images of students to be recognized.
The number of the image collectors 1 may be one or multiple, and the present invention is not limited in particular.
In order to achieve the face recognition effect, the image collector 1 needs to collect a plurality of face images, for example, at least 10 continuous face images are needed, and of course, the number of the continuously collected face images may be selected according to actual needs, which is not limited in the present invention.
The recognition terminal 2 is configured to perform feature point extraction according to the received multiple face images to obtain a salient feature point set, and send the salient feature point set to a server.
Specifically, the identification terminal 2 is specifically configured to use an Eigenface algorithm to extract feature points of each face image from the multiple face images, denoise the feature points of each extracted face image, strip out obviously abnormal feature points, and fuse the feature points of the multiple faces into a set of significant feature points of the student to be identified by an average distance method.
In this embodiment, Eigenface is the name of a set of feature vectors used in computer vision problems for face recognition. The method for identifying by using the feature plane is developed by Sirovich and Kirby and is used in face classification by Matthew Turk and Alex Pentland. The feature vector is derived from a covariance matrix of probability distribution on a high-dimensional vector space of the face image. The eigenplanes themselves constitute the basis set for all images used to construct the covariance matrix. Of course, it should be noted that in other embodiments of the present invention, other feature extraction algorithms may also be used to obtain feature points of each face image, such as a histogram.
In this embodiment, the method of averaging the distances is to randomly extract k pairs of points equally probabilistically from Cn2 points, then calculate the distances for the k pairs of points, calculate k distances in total, and then estimate the average distance of n points by using the average value of the k distances.
In the fusion, the distance between each feature point and a central point (the central point can be obtained by clustering, for example) can be obtained, and the distance from the central point is compared with the average distance to determine whether the feature point is a significant feature point, wherein if the distance between the feature point and the central point is greater than the average distance, the feature point is not a significant feature point, otherwise, the feature point is a significant feature point.
In this embodiment, the salient feature point set may have 1024 salient feature points, where the 1024 salient feature points include an optimal facial feature point recognition set, and matching is performed through the dimensions of the facial feature point recognition set. Of course, in other embodiments of the present invention, the number of feature points in the salient feature point set may also be determined according to actual needs, and the present invention is not limited specifically.
The server 3 is used for matching the salient feature point set according to a prestored face feature point database to obtain student information corresponding to the salient feature point set and returning the student information to the identification terminal 2; the face feature point database is used for storing feature point sets of students in the current school and corresponding student information.
Specifically, the matching of features is performed on feature descriptors, which are usually vectors, and the distance between two feature descriptors can reflect the similarity, that is, the two feature points are not the same. Depending on the descriptor, different distance metrics may be selected. If the descriptor is a floating point type descriptor, the Euclidean distance of the descriptor can be used; the descriptor BRIEF for a binary may use its hamming distance (hamming distance between two different binary refers to the number of different bits of two binary strings). With the method of calculating the similarity of descriptors, the feature point most similar to the descriptor is found in the feature point set, that is, the feature point is matched.
In this embodiment, the face feature point database is formed by extracting feature points from face photographs of each student in advance. When extracting, more than 50 face photos can be collected for each student, the face photos comprise different postures, facial expressions and illumination, and the features of the face photos are fused to generate a feature point set corresponding to each student, so that a final face feature point database is formed according to the feature point set of each student.
The system provided by the embodiment of the invention has the following advantages:
in the embodiment of the invention, a plurality of face images of students to be recognized are collected through an image collector 1, the plurality of face images are sent to a recognition terminal 2, feature points are extracted according to the received plurality of face images to obtain a significant feature point set, the significant feature point set is sent to a server 3, the significant feature point set is matched according to a prestored face feature point database to obtain student information corresponding to the significant feature point set, and the student information is returned to the recognition terminal 3 to complete face recognition, so that the error rate of face recognition can be reduced and the recognition speed can be improved.
On the basis of the above embodiment, there may be a case where a plurality of pieces of student information are simultaneously matched, for example, if there are long similar students or twins in a school, the attendance cannot be accurately performed at this time.
To this end, in a preferred embodiment of the present invention, the identification terminal 2 further includes a speaker 4 and a fingerprint collector 5; when the acquired student information is multiple pieces, the identification terminal 2 is further configured to:
a loudspeaker 4 is started to prompt the student to input fingerprint information through a fingerprint collector 5, and the collected fingerprint information is sent to the server 3; the server 3 is further configured to select corresponding student information from the acquired pieces of student information according to the fingerprint information, and send the student information to the identification terminal 2.
The system can accurately check the attendance aiming at the conditions of students or twins and the like with similar growth in schools.
On the basis of the above embodiment, there may be a case where a plurality of pieces of student information are simultaneously matched, for example, if there are long similar students or twins in a school, the attendance cannot be accurately performed at this time.
To this end, in another preferred embodiment of the invention,
when the obtained student information is multiple pieces, and when the obtained student information is multiple pieces, the server 3 is further configured to extract an eye print feature corresponding to an eye region of a face from the face image, input the eye print feature into a pre-established neural network to obtain an identification result of the neural network on the eye print feature, and send the student information corresponding to the identification result to the identification terminal.
Although students or twins with similar growth have similar external forms, the eye print characteristics (the eye print refers to the blood vessel arrangement on the white eye) still have individual differences, so that the accuracy of campus attendance can be improved by selecting the eye print to identify the attendance.
On the basis of the above embodiment, considering that the students are in the growth stage, the change speed of the body and face is also high.
For this purpose, the server 3 is further configured to update, at predetermined intervals, information of the feature point sets corresponding to the students in the face database according to the salient feature point sets.
For example, assuming that the feature point set of student a is denoted as a1 in the database, and the salient feature point set of student a collected on a certain day is a2, the updated feature point set of student a is denoted as A3.
Wherein, A3 is n a1+ m a 2. Wherein n + m is 1.
The updating speed can be controlled by setting the values of n and m, for example, when the growth speed is fast in the primary school stage, the value of m can be set to be larger; when the growth rate is low in the university stage and the development is mature, the value of m can be set to be small.
The working process of the campus attendance system based on face recognition in the embodiment is as follows: the method comprises the steps that when a student to be recognized enters a recognition area, a plurality of face images of the student to be recognized are collected and sent to a recognition terminal, the recognition terminal extracts feature points according to the received face images to obtain a significant feature point set, and the significant feature point set is sent to a server 3; the server 3 matches the salient feature point set according to a prestored face feature point database to obtain student information corresponding to the salient feature point set, and returns the student information to the identification terminal 2; the face feature point database is used for storing feature point sets of students in the current school and corresponding student information.
Referring to fig. 3, fig. 3 is a schematic flow chart of a campus attendance system with face recognition according to an embodiment of the present invention, which describes in detail how a face image is recognized, and details are as follows:
s301: a plurality of face images are collected through the image collector 1, and a face model is built through the collection of various postures.
S302: the recognition terminal 2 extracts feature points according to the received multiple face images to obtain a salient feature point set, and sends the salient feature point set to the server 3;
s303: the server 3 matches the salient feature point set according to a prestored face feature point database to obtain student information corresponding to the salient feature point set, and returns the student information to the identification terminal; the face feature point database is used for storing feature point sets of students in the current school and corresponding student information.
In this embodiment, the identification terminal 2 is specifically configured to use an Eigenface algorithm to extract feature points of each face image from the plurality of face images respectively; denoising the extracted feature points of each human face image, stripping the feature points with obvious abnormality, and fusing the feature points of a plurality of human faces by an average distance method to generate a set of obvious feature points of students to be recognized; and the full school face library is combined for further model training and judgment, so that the recognition speed is improved.
In this embodiment, when the face image recognition result matches with a pre-stored face image, it is determined that the face recognition is passed; and when the face image recognition result is not matched with the pre-stored face image, determining that the face recognition is not passed. Here, the face image recognition result is matched with the pre-stored face image as: the extraction points of the features of the face image information are consistent with the pre-stored face image model.
As can be seen from the above embodiment, the Eigenface algorithm is used to extract the feature points of each face image from the plurality of face images; the extracted feature points of each human face image are denoised, the feature points with obvious abnormity are stripped, and then the feature points of a plurality of human faces are fused by an average distance method to generate a set of the obvious feature points of the students to be recognized, so that the difficulty of the error rate of human face recognition can be solved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a recognition terminal based on face recognition according to another embodiment of the present invention, which describes in detail a process of recognizing a situation that multiple pieces of student information are simultaneously matched, and details are as follows:
s401: to the many student information that appear matching simultaneously, start loudspeaker 4 and remind the student to type in its fingerprint information through fingerprint sampler 5.
S402: and sending the collected fingerprint information to the server 3.
In this embodiment, the purpose of entering the fingerprint through the fingerprint collector 5 is to perform attendance in a manner of fingerprint identification under the condition that the attendance cannot be accurately performed due to the fact that long and similar students or twins exist in a school.
Referring to fig. 5, fig. 5 is a functional block diagram of a campus attendance system based on face recognition according to an embodiment of the present invention, where the functional block diagram of the campus attendance system based on face recognition includes:
the preprocessing module 501 stores the face images of the teachers and students in the universities in the memory of the server;
the feature extraction module 502 is used for extracting feature points of a plurality of face images of an object to be recognized;
the training module 503 is used for performing model training and judgment according to the face model and the full school face image;
the face recognition judging module 504 returns the result of the face recognition judgment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The utility model provides a campus attendance system based on face identification which characterized in that includes:
the system comprises an image collector, an identification terminal and a server;
the image collector is used for collecting a plurality of face images of a student to be identified and sending the face images to the identification terminal;
the identification terminal is used for extracting the characteristic points according to the received multiple face images to obtain a salient characteristic point set and sending the salient characteristic point set to the server;
the server is used for matching the salient feature point set according to a prestored face feature point database to obtain student information corresponding to the salient feature point set and returning the student information to the identification terminal; the face feature point database is used for storing feature point sets of students in the current school and corresponding student information.
2. The campus attendance system based on face recognition according to claim 1, wherein the recognition terminal is specifically configured to extract feature points of each face image from the plurality of face images respectively using an Eigenface algorithm, denoise the extracted feature points of each face image, strip out obviously abnormal feature points, and fuse the feature points of the plurality of faces by an average distance method to generate a set of significant feature points of students to be recognized.
3. The face recognition-based campus attendance system of claim 2 wherein the set of salient feature points has 1024 salient feature points, the 1024 salient feature points comprising the best set of facial feature point recognitions.
4. The face recognition-based campus attendance system of claim 1,
the identification terminal also comprises a fingerprint collector and a loudspeaker;
when the acquired student information is multiple, the identification terminal is further used for starting a loudspeaker to prompt the student to input the fingerprint information of the student through the fingerprint collector and sending the collected fingerprint information to the server;
the server is further used for selecting corresponding student information from the plurality of pieces of student information according to the fingerprint information and sending the student information to the identification terminal.
5. The campus attendance system based on face recognition according to claim 1, wherein when the obtained student information is plural, the server is further configured to extract an eye print feature corresponding to an eye region of a face from the face image, input the eye print feature into a pre-established neural network to obtain a recognition result of the eye print feature by the neural network, and send the student information corresponding to the recognition result to the recognition terminal.
6. The face recognition-based campus attendance system of claim 1, wherein the server is further configured to update information of the feature point sets corresponding to the students in the face database according to the salient feature point sets at predetermined time intervals.
7. The face recognition-based campus attendance system of claim 6, wherein the update speed of the feature point set is determined according to the age groups of different students, and different update speeds correspond to different update weights.
8. The automatic attendance system based on face recognition of claim 2, wherein the recognition terminal is configured to perform feature point extraction on the faces of the students, extract at least 10 avatar feature points, and normalize the extracted feature points into a 1024-dimensional feature point set by a distance averaging method.
9. The automatic attendance system based on the face recognition as claimed in claim 3, wherein the server is specifically configured to load feature point sets of all students in the school in advance in the memory, match the salient feature point set with feature points of all students through an algorithm of a minimum distance of feature points, and return a final result to the recognition terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910701123.0A CN110443577A (en) | 2019-07-31 | 2019-07-31 | A kind of campus attendance checking system based on recognition of face |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910701123.0A CN110443577A (en) | 2019-07-31 | 2019-07-31 | A kind of campus attendance checking system based on recognition of face |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110443577A true CN110443577A (en) | 2019-11-12 |
Family
ID=68432504
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910701123.0A Pending CN110443577A (en) | 2019-07-31 | 2019-07-31 | A kind of campus attendance checking system based on recognition of face |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110443577A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112001219A (en) * | 2020-06-19 | 2020-11-27 | 国家电网有限公司技术学院分公司 | Multi-angle multi-face recognition attendance checking method and system |
| CN112809697A (en) * | 2021-01-11 | 2021-05-18 | 福建思特电子有限公司 | 5G intelligent entrance guard robot |
| CN113343793A (en) * | 2021-05-24 | 2021-09-03 | 浪潮软件股份有限公司 | National student status-based face real-name authentication method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105469455A (en) * | 2014-09-12 | 2016-04-06 | 航天信息股份有限公司 | Human face identification attendance check management method based on mobile terminal and human face identification attendance check management system based on mobile terminal |
| US20160132718A1 (en) * | 2014-11-06 | 2016-05-12 | Intel Corporation | Face recognition using gradient based feature analysis |
| CN107305624A (en) * | 2016-04-20 | 2017-10-31 | 厦门中控智慧信息技术有限公司 | A kind of person recognition method and device based on multi-mode biometric information |
| CN109003346A (en) * | 2018-07-13 | 2018-12-14 | 河海大学 | A kind of campus Work attendance method and its system based on face recognition technology |
-
2019
- 2019-07-31 CN CN201910701123.0A patent/CN110443577A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105469455A (en) * | 2014-09-12 | 2016-04-06 | 航天信息股份有限公司 | Human face identification attendance check management method based on mobile terminal and human face identification attendance check management system based on mobile terminal |
| US20160132718A1 (en) * | 2014-11-06 | 2016-05-12 | Intel Corporation | Face recognition using gradient based feature analysis |
| CN107305624A (en) * | 2016-04-20 | 2017-10-31 | 厦门中控智慧信息技术有限公司 | A kind of person recognition method and device based on multi-mode biometric information |
| CN109003346A (en) * | 2018-07-13 | 2018-12-14 | 河海大学 | A kind of campus Work attendance method and its system based on face recognition technology |
Non-Patent Citations (2)
| Title |
|---|
| 卓金武: "《MATLAB在数学建模中的应用》", 30 September 2014, 北京航空航天大学出版社 * |
| 朱道明等: "《建筑安防技术》", 31 January 2013 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112001219A (en) * | 2020-06-19 | 2020-11-27 | 国家电网有限公司技术学院分公司 | Multi-angle multi-face recognition attendance checking method and system |
| CN112001219B (en) * | 2020-06-19 | 2024-02-09 | 国家电网有限公司技术学院分公司 | Multi-angle multi-face recognition attendance checking method and system |
| CN112809697A (en) * | 2021-01-11 | 2021-05-18 | 福建思特电子有限公司 | 5G intelligent entrance guard robot |
| CN113343793A (en) * | 2021-05-24 | 2021-09-03 | 浪潮软件股份有限公司 | National student status-based face real-name authentication method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108009528B (en) | Triple Loss-based face authentication method and device, computer equipment and storage medium | |
| CN108921100B (en) | Face recognition method and system based on visible light image and infrared image fusion | |
| US20170262472A1 (en) | Systems and methods for recognition of faces e.g. from mobile-device-generated images of faces | |
| CN101216884A (en) | A method and system for face authentication | |
| CN105740808B (en) | Face identification method and device | |
| CN110443577A (en) | A kind of campus attendance checking system based on recognition of face | |
| CN111368772A (en) | Identity recognition method, device, equipment and storage medium | |
| CN112613480A (en) | Face recognition method, face recognition system, electronic equipment and storage medium | |
| CN109635625B (en) | Intelligent identity verification method, equipment, storage medium and device | |
| CN106529377A (en) | Age estimating method, age estimating device and age estimating system based on image | |
| CN104598888A (en) | Human face gender recognition method | |
| CN116363712B (en) | Palmprint palm vein recognition method based on modal informativity evaluation strategy | |
| CN110827432A (en) | Class attendance checking method and system based on face recognition | |
| CN113076886A (en) | Face individual identification device and method for cat | |
| Wu et al. | Kinship verification from faces: Methods, databases and challenges | |
| CN112749605A (en) | Identity recognition method, system and equipment | |
| US20140025624A1 (en) | System and method for demographic analytics based on multimodal information | |
| CN110321871B (en) | Palm vein identification system and method based on LSTM | |
| Mau et al. | Video face matching using subset selection and clustering of probabilistic multi-region histograms | |
| CN112418078B (en) | Score modulation method, face recognition device and medium | |
| CN111428670B (en) | Face detection method, face detection device, storage medium and equipment | |
| CN108710838B (en) | Thermal infrared facial image recognition method under a kind of overnight sight | |
| Lateef et al. | Face Recognition-Based Automatic Attendance System in a Smart Classroom | |
| CN111428597A (en) | Attendance management method based on face recognition | |
| Pavani et al. | A confidence-based update rule for self-updating human face recognition systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 361000 unit 2202, No. 63, Chengyi North Street, phase III, software park, Xiamen City, Fujian Province Applicant after: Century Haihang (Xiamen) Technology Co.,Ltd. Address before: 391000 Pioneer Building, 1302 Jimei Avenue, Jimei District, Xiamen City, Fujian Province Applicant before: Century Haihang (Xiamen) Technology Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191112 |
|
| RJ01 | Rejection of invention patent application after publication |