CN107590452A - A kind of personal identification method and device based on gait and face fusion - Google Patents
A kind of personal identification method and device based on gait and face fusion Download PDFInfo
- Publication number
- CN107590452A CN107590452A CN201710786656.4A CN201710786656A CN107590452A CN 107590452 A CN107590452 A CN 107590452A CN 201710786656 A CN201710786656 A CN 201710786656A CN 107590452 A CN107590452 A CN 107590452A
- Authority
- CN
- China
- Prior art keywords
- face
- quality evaluation
- gait
- human body
- facial image
- 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
Landscapes
- Image Analysis (AREA)
Abstract
The present invention relates to a kind of gait based on deep learning and face fusion personal identification method and device, wherein device includes video acquisition and pretreatment module, Method of Gait Feature Extraction module, face characteristic extraction module and identification module, method includes collection video flowing, pedestrian detection and tracking, and Face datection are carried out to video flowing；Method of Gait Feature Extraction is carried out to the human body image, and calculates the quality evaluation fraction of the gait feature；Face characteristic extraction is carried out to facial image, and calculates the quality evaluation fraction of face characteristic, using quality evaluation fraction highest facial image as facial image to be identified；Gait feature and face characteristic are weighted according to respective quality score, input SVM classifier carries out identification.By the quality evaluation fraction for calculating gait feature and face characteristic, and be weighted according to quality score, combine the advantage of recognition of face and Gait Recognition, the complementation of two kinds of identification technologies, the robustness of identifying system is added, improves the accuracy of piece identity's identification.
Description
Technical field
The present invention relates to biometrics identification technology field, and in particular to a kind of to be known based on the identity of gait and face fusion
Other method and device.
Background technology
Biometrics identification technology is a kind of technology that identification is carried out using human body biological characteristics.These biological characteristics
Including：Fingerprint, palmmprint, iris, face etc., extensively using various peaces such as security protection, bank, customs's frontier inspection, estate managements
In full field, the accuracy rate for improving identification greatly reduces man power and material again, is increasingly favored by various circles of society.
Recognition of face is a kind of contactless biometric identification, it is not necessary to which the behavior of people coordinates, especially suitable for long distance
From identification, can be used for intelligent video monitoring system.Current face recognition technology, when target person side is to camera
When, camera can only collect the side face of target person, and recognition accuracy can significantly decline.On the other hand, according to people
Posture on foot identifies the technology of target person identity (being also Gait Recognition), has non-contact remote and is not easy to pretend
The features such as, increasing researcher's concern has been obtained in recent years.But when target person face camera, due to
The posture of target person can not be obtained well, and recognition effect is had a greatly reduced quality.
The content of the invention
The present invention is directed to technical problem of the prior art, there is provided a kind of identification side based on gait and face fusion
Method and device, by combining Gait Recognition and recognition of face, complementation is carried out to two kinds of identification technologies, to improve piece identity's identification
Accuracy.
The technical scheme that the present invention solves above-mentioned technical problem is as follows：
On the one hand, the present invention provides a kind of personal identification method based on gait and face fusion, comprises the following steps：
S1, video flowing is gathered, pedestrian detection and tracking are carried out to video flowing, obtain human body frame image sequence, while to people
Body two field picture carries out Face datection, obtains human face image sequence corresponding to human body frame image sequence；
S2, Method of Gait Feature Extraction is carried out to the human body image in the human body frame image sequence, and obtain quality evaluation ginseng
Number, the quality evaluation fraction of the gait feature is calculated according to quality evaluation parameter；
S3, face characteristic extraction is carried out to each image in human face image sequence, and obtain the quality evaluation of facial image
Parameter, the quality evaluation fraction of face characteristic is calculated according to quality evaluation parameter, by quality evaluation fraction highest facial image
As facial image to be identified；
S4, gait feature and face characteristic are weighted according to respective quality score, input SVM classifier carries out body
Part identification.
The beneficial effects of the invention are as follows：By calculating the quality evaluation fraction of gait feature and face characteristic, and gait
Feature and face characteristic are weighted according to respective quality score, combine the advantage of recognition of face and Gait Recognition, two kinds
The complementation of identification technology, the robustness of identifying system is added, improve the accuracy of piece identity's identification.
On the basis of above-mentioned technical proposal, the present invention can also do following improvement.
Further, pedestrian detection is carried out to video flowing described in step S1, using Faster-RCNN deep learning side
Method is realized, while uses correlation filtering, to detecting that pedestrian is tracked；The Face datection uses SSD single detectors
Capture face.
It is using the above-mentioned further beneficial effect of scheme：Faster-RCNN deep learning method is than common use
Edge feature, shape facility, statistical nature etc. are more accurate, there has also been very big lifting in processing ground speed, it is possible to achieve real
When the demand that handles.Correlation filtering is used simultaneously, to detecting that pedestrian is tracked, with the real-time of lifting processing.SSD
(Single Shot MultiBox Detector) is to realize Target detection and identification using single deep neural network model
Method.Combine Faster R-CNN anchor and YOLO single Neurals detection thinking, it is possible to achieve high-accuracy is examined in real time
Survey.
Preferably, the step S2 includes following sub-step：
S21, size normalization processing is carried out to the human body image in human body frame image sequence；
S22, the human body frame image sequence input gestures after processing are identified into network, extract the posture of each human body image
Feature, posture characteristic sequence corresponding to human body frame image sequence is generated, while extract the quality evaluation parameter of human body two field picture, and
The quality evaluation fraction of human body two field picture is calculated according to quality evaluation parameter；
S23, the posture characteristic sequence is inputted into Gait Recognition network, obtain for describe the feature of body gait to
Amount.
Preferably, the step S3, including following sub-step：
S31, Face datection is carried out using SSD singles detector to each facial image in human face image sequence, carried
The 3D Landmark of every face are taken, while obtain the quality evaluation parameter of facial image, and are commented according to the quality of facial image
Valency parameter calculates the quality evaluation fraction of every facial image；
S32, choose human face image sequence in quality evaluation fraction highest facial image as facial image to be identified, and
Three-dimensional modeling is carried out according to the 3D Landmark of the facial image to be identified, projects front face；
S33, the characteristic vector of facial image to be identified is obtained by face characteristic neutral net.
Further, the quality evaluation parameter includes size, angle and the definition of human body image or facial image.
On the other hand, the present invention provides a kind of identity recognition device based on gait and face fusion, including：
Video acquisition and pretreatment module, for gathering video flowing, pedestrian detection and tracking are carried out to video flowing, obtain people
Body frame image sequence, while Face datection is carried out to human body two field picture, obtain facial image sequence corresponding to human body frame image sequence
Row；
Method of Gait Feature Extraction module, Method of Gait Feature Extraction is carried out to the human body image in the human body frame image sequence, and
Quality evaluation parameter is obtained, the quality evaluation fraction of the gait feature is calculated according to quality evaluation parameter；
Face characteristic extraction module, face characteristic extraction is carried out to each image in human face image sequence, and obtain face
The quality evaluation parameter of image, the quality evaluation fraction of face characteristic is calculated according to quality evaluation parameter, by quality evaluation fraction
Highest facial image is as facial image to be identified；
Identification module, gait feature and face characteristic are weighted according to respective quality score, input SVM classifier
Carry out identification.
The beneficial effects of the invention are as follows：By calculating the quality evaluation fraction of gait feature and face characteristic, and gait
Feature and face characteristic are weighted according to respective quality score, combine the advantage of recognition of face and Gait Recognition, two kinds
The complementation of identification technology, the robustness of identifying system is added, improve the accuracy of piece identity's identification.
Further, the video acquisition and pretreatment module use Faster-RCNN deep learning method to video flowing
Pedestrian detection is carried out, while uses correlation filtering, to detecting that pedestrian is tracked；Face is carried out using SSD singles detector
Detection, capture face.
Beneficial effect using above-mentioned further scheme is that Faster-RCNN deep learning method is than common use
Edge feature, shape facility, statistical nature etc. are more accurate, there has also been very big lifting in processing ground speed, it is possible to achieve real
When the demand that handles.Correlation filtering is used simultaneously, to detecting that pedestrian is tracked, with the real-time of lifting processing.SSD
(Single Shot MultiBox Detector) is to realize Target detection and identification using single deep neural network model
Method.Combine Faster R-CNN anchor and YOLO single Neurals detection thinking, it is possible to achieve high-accuracy is examined in real time
Survey.
Preferably, the Method of Gait Feature Extraction module is specifically used for：
Size normalization processing is carried out to the human body image in human body frame image sequence；
Human body frame image sequence input gestures after processing are identified into network, the posture for extracting each human body image is special
Sign, posture characteristic sequence corresponding to human body frame image sequence is generated, while extract the quality evaluation parameter of human body two field picture, and root
The quality evaluation fraction of human body two field picture is calculated according to quality evaluation parameter；
The posture characteristic sequence is inputted into Gait Recognition network, obtains the characteristic vector for describing body gait.
Preferably, the face characteristic extraction module is specifically used for：
Face datection is carried out using SSD singles detector to each facial image in human face image sequence, extraction is every
The 3D Landmark of face are opened, while obtain the quality evaluation parameter of facial image, and are joined according to the quality evaluation of facial image
Number calculates the quality evaluation fraction of every facial image；
Choose human face image sequence in quality evaluation fraction highest facial image as facial image to be identified, and according to
The 3D Landmark of the facial image to be identified carry out three-dimensional modeling, project front face；
The characteristic vector of facial image to be identified is obtained by face characteristic neutral net.
Preferably, the quality evaluation parameter includes size, angle and the definition of human body image or facial image.
Brief description of the drawings
Fig. 1 is a kind of personal identification method flow chart based on gait and face fusion provided in an embodiment of the present invention；
Fig. 2 is gait feature abstracting method flow chart provided in an embodiment of the present invention；
Fig. 3 is face feature extraction method flow chart provided in an embodiment of the present invention；
Fig. 4 is weighted calculation provided in an embodiment of the present invention and personal identification method flow chart；
Fig. 5 is a kind of identity recognition device structure chart based on gait and face fusion provided in an embodiment of the present invention；
Embodiment
The principle and feature of the present invention are described below in conjunction with example, the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the present invention.
Fig. 1 is a kind of personal identification method flow chart based on gait and face fusion provided in an embodiment of the present invention.Such as
Shown in Fig. 1, comprise the following steps：
S1, video flowing is gathered, pedestrian detection and tracking are carried out to video flowing, obtain human body frame image sequence, while to people
Body two field picture carries out Face datection, obtains human face image sequence corresponding to human body frame image sequence；
S2, Method of Gait Feature Extraction is carried out to the human body image in the human body frame image sequence, and obtain quality evaluation ginseng
Number, the quality evaluation fraction of the gait feature is calculated according to quality evaluation parameter；Referring specifically to Fig. 2；
S3, face characteristic extraction is carried out to each image in human face image sequence, and obtain the quality evaluation of facial image
Parameter, the quality evaluation fraction of face characteristic is calculated according to quality evaluation parameter, by quality evaluation fraction highest facial image
As facial image to be identified；Referring specifically to Fig. 3；
S4, gait feature and face characteristic are weighted according to respective quality score, input SVM classifier carries out body
Part identification, referring specifically to Fig. 4.
It is the angle of the quality evaluation parameter including image, size, definition, contrast, bright in above-described embodiment
The factor parameter of picture quality is influenceed as defined in the ISO/IEC19794-5 such as degree, definition standards, by calculate gait feature and
The quality evaluation fraction of face characteristic, and gait feature and face characteristic are weighted according to respective quality score, it is comprehensive
The advantage of recognition of face and Gait Recognition, the complementations of two kinds of identification technologies, the robustness of identifying system is added, improves personage
The accuracy of identification.According to ISO/IEC19794-5 standards, the factor that influences picture quality is fuzzy, non-frontal image,
Side illumination, by a variety of evaluation indexes such as the contrast of image, lightness, definition, the size of face, position and angles
Carry out overall merit facial image, draw respective evaluation coefficient (0~1.0), then by each coefficient proportion (1~
100) it is weighted, finally draws the evaluation score of every width figure, so as to selects the optimal facial image of quality.
On the basis of above-described embodiment, pedestrian detection is carried out to video flowing described in step S1, using Faster-
RCNN deep learning method is realized, while uses correlation filtering, to detecting that pedestrian is tracked；The Face datection is adopted
Face is captured with SSD singles detector.
In above-described embodiment, Faster-RCNN deep learning method than it is common using edge feature, shape facility,
Statistical nature etc. is more accurate, there has also been very big lifting in processing ground speed, it is possible to achieve the demand handled in real time.Adopt simultaneously
With correlation filtering, to detecting that pedestrian is tracked, with the real-time of lifting processing.SSD(Single Shot MultiBox
Detector) it is method that Target detection and identification is realized using single deep neural network model.Combine Faster R-
CNN anchor and YOLO single Neurals detection thinking, it is possible to achieve high-accuracy detects in real time.
Preferably, the step S2 includes following sub-step, as shown in Fig. 2：
S21, size normalization processing is carried out to the human body image in human body frame image sequence；
S22, the human body frame image sequence input gestures after processing are identified into network, extract the posture of each human body image
Feature, posture characteristic sequence corresponding to human body frame image sequence is generated, while extract the quality evaluation parameter of human body two field picture, and
The quality evaluation fraction of human body two field picture is calculated according to quality evaluation parameter；
S23, the posture characteristic sequence is inputted into Gait Recognition network, obtain for describe the feature of body gait to
Amount.
Preferably, the step S3, including following sub-step, as shown in Figure 3：
S31, Face datection is carried out using SSD singles detector to each facial image in human face image sequence, carried
The 3D Landmark of every face are taken, while obtain the quality evaluation parameter of facial image, and are commented according to the quality of facial image
Valency parameter calculates the quality evaluation fraction of every facial image；
S32, choose human face image sequence in quality evaluation fraction highest facial image as facial image to be identified, and
Three-dimensional modeling is carried out according to the 3D Landmark of the facial image to be identified, projects front face；
S33, the characteristic vector of facial image to be identified is obtained by face characteristic neutral net.
Preferably, the quality evaluation parameter includes size, angle and the definition of human body image or facial image.
On the other hand, the present invention provides a kind of identity recognition device based on gait and face fusion, as shown in figure 5, bag
Include：
Video acquisition and pretreatment module, for gathering video flowing, pedestrian detection and tracking are carried out to video flowing, obtain people
Body frame image sequence, while Face datection is carried out to human body two field picture, obtain facial image sequence corresponding to human body frame image sequence
Row；
Method of Gait Feature Extraction module, Method of Gait Feature Extraction is carried out to the human body image in the human body frame image sequence, and
Quality evaluation parameter is obtained, the quality evaluation fraction of the gait feature is calculated according to quality evaluation parameter；
Face characteristic extraction module, face characteristic extraction is carried out to each image in human face image sequence, and obtain face
The quality evaluation parameter of image, the quality evaluation fraction of face characteristic is calculated according to quality evaluation parameter, by quality evaluation fraction
Highest facial image is as facial image to be identified；
Identification module, gait feature and face characteristic are weighted according to respective quality score, input SVM classifier
Carry out identification.
In above-described embodiment, by calculating the quality evaluation fraction of gait feature and face characteristic, and gait feature and
Face characteristic is weighted according to respective quality score, combines the advantage of recognition of face and Gait Recognition, two kinds of identification skills
The complementation of art, the robustness of identifying system is added, improve the accuracy of piece identity's identification.Marked according to ISO/IEC19794-5
Standard, the factor for influenceing quality of human face image is fuzzy, non-frontal image, side illumination, by the contrast to image, is become clear
A variety of evaluation indexes such as degree, definition, the size of face, position and angle carry out overall merit facial image, draw respective comment
Valency coefficient (0~1.0), then it is weighted by each coefficient proportion (1~100), finally draws every width figure
Score is evaluated, so as to select the optimal facial image of quality.
On the basis of above-described embodiment, the video acquisition and pretreatment module use Faster-RCNN depth
Learning method carries out pedestrian detection to video flowing, while uses correlation filtering, to detecting that pedestrian is tracked；Using SSD singles
Detector carries out Face datection, captures face.
In above-described embodiment, Faster-RCNN deep learning method than it is common using edge feature, shape facility,
Statistical nature etc. is more accurate, there has also been very big lifting in processing ground speed, it is possible to achieve the demand handled in real time.Adopt simultaneously
With correlation filtering, to detecting that pedestrian is tracked, with the real-time of lifting processing.SSD(Single Shot MultiBox
Detector) it is method that Target detection and identification is realized using single deep neural network model.Combine Faster R-
CNN anchor and YOLO single Neurals detection thinking, it is possible to achieve high-accuracy detects in real time.
Preferably, the Method of Gait Feature Extraction module is specifically used for：
Size normalization processing is carried out to the human body image in human body frame image sequence；
Human body frame image sequence input gestures after processing are identified into network, the posture for extracting each human body image is special
Sign, posture characteristic sequence corresponding to human body frame image sequence is generated, while extract the quality evaluation parameter of human body two field picture, and root
The quality evaluation fraction of human body two field picture is calculated according to quality evaluation parameter；
The posture characteristic sequence is inputted into Gait Recognition network, obtains the characteristic vector for describing body gait.
Preferably, the face characteristic extraction module is specifically used for：
Face datection is carried out using SSD singles detector to each facial image in human face image sequence, extraction is every
The 3D Landmark of face are opened, while obtain the quality evaluation parameter of facial image, and are joined according to the quality evaluation of facial image
Number calculates the quality evaluation fraction of every facial image；
Choose human face image sequence in quality evaluation fraction highest facial image as facial image to be identified, and according to
The 3D Landmark of the facial image to be identified carry out three-dimensional modeling, project front face；
The characteristic vector of facial image to be identified is obtained by face characteristic neutral net.
Preferably, the quality evaluation parameter includes size, angle and the definition of human body image or facial image.
The gesture recognition model that we are obtained with 2 kinds of differences based on different pieces of information collection (PFM and H3.6M) training respectively, with
And same Gait Recognition model, do 2 kinds of tests.The first test, is determined with the sequence for containing 10 continuous postures
A certain personal gait, 305 people (wherein 100 people are used to train, and 150 people are used to verify, 155 are used to test) are shared, everyone
Clothing is consistent, but has three kinds of walking manners, including：Normal walking, knapsack and wears rubber overshoes.
The Gait Recognition accuracy rate of the test of table 11
| Normal walking | Knapsack | Wear rubber overshoes | It is average | |
| H3.6M | 99.4% | 92.6% | 98.7% | 96.9% |
| PFM | 99.7% | 99.0% | 99.0% | 99.2% |
Second of test, determines a certain personal gait with the sequence for containing 20 continuous postures, shares 32 people (10
Individual is used for training pattern, and 6 people are used to verify, 16 people are used to test), everyone wears Various Seasonal, equally including three
Kind walking manner.
The Gait Recognition accuracy rate of the test of table 22
| Normal walking | Knapsack | Wear rubber overshoes | It is average | |
| H3.6M | 74.2% | 89.1% | 82.8% | 80.1% |
| PFM | 78.1% | 56.3% | 46.9% | 60.4% |
In terms of the test result of Tables 1 and 2, the gait for capturing pedestrian by depth learning technology has certain robust
Property, obtained attitude mode is especially trained by H3.6M data sets, is more suitable for that the pedestrian of different garment is identified.
Finally recognition of face and Gait Recognition are combined mode by us, go to test 32 people above again, finally
Test result is as shown in table 3, and the mode combined with both is significantly lifted to identify that pedestrian has in accuracy rate.
The recognition of face of table 3 and the accuracy rate of Gait Recognition
| Normal walking | Knapsack | Wear rubber overshoes | It is average | |
| H3.6M | 97.2% | 98.5% | 99.0% | 98.2% |
| PFM | 95.1% | 89.3% | 85.9% | 90.1% |
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all the present invention spirit and
Within principle, any modification, equivalent substitution and improvements made etc., it should be included in the scope of the protection.
Claims (10)
1. a kind of personal identification method based on gait and face fusion, it is characterised in that comprise the following steps：
S1, video flowing is gathered, pedestrian detection and tracking are carried out to video flowing, obtain human body frame image sequence, while to human body frame
Image carries out Face datection, obtains human face image sequence corresponding to human body frame image sequence；
S2, Method of Gait Feature Extraction is carried out to the human body image in the human body frame image sequence, and obtain quality evaluation parameter, root
The quality evaluation fraction of the gait feature is calculated according to quality evaluation parameter；
S3, face characteristic extraction is carried out to each image in human face image sequence, and obtains the quality evaluation parameter of facial image,
The quality evaluation fraction of face characteristic is calculated according to quality evaluation parameter, using quality evaluation fraction highest facial image as treating
Identify facial image；
S4, gait feature and face characteristic are weighted according to respective quality score, input SVM classifier carries out identity knowledge
Not.
A kind of 2. personal identification method based on gait and face fusion according to claim 1, it is characterised in that step
Pedestrian detection is carried out to video flowing described in S1, realized using Faster-RCNN deep learning method, while use phase
Filtering is closed, to detecting that pedestrian is tracked；The Face datection is using SSD singles detector crawl face.
3. a kind of personal identification method based on gait and face fusion according to claim 2, it is characterised in that described
Step S2 includes following sub-step：
S21, size normalization processing is carried out to the human body image in human body frame image sequence；
S22, the human body frame image sequence input gestures after processing are identified into network, the posture for extracting each human body image is special
Sign, posture characteristic sequence corresponding to human body frame image sequence is generated, while extract the quality evaluation parameter of human body two field picture, and root
The quality evaluation fraction of human body two field picture is calculated according to quality evaluation parameter；
S23, the posture characteristic sequence is inputted into Gait Recognition network, obtains the characteristic vector for describing body gait.
4. a kind of personal identification method based on gait and face fusion according to claim 3, it is characterised in that described
Step S3, including following sub-step：
S31, Face datection is carried out using SSD singles detector to each facial image in human face image sequence, extraction is every
The 3D Landmark of face are opened, while obtain the quality evaluation parameter of facial image, and are joined according to the quality evaluation of facial image
Number calculates the quality evaluation fraction of every facial image；
S32, choose human face image sequence in quality evaluation fraction highest facial image as facial image to be identified, and according to
The 3D Landmark of the facial image to be identified carry out three-dimensional modeling, project front face；
S33, the characteristic vector of facial image to be identified is obtained by face characteristic neutral net.
5. a kind of personal identification method based on gait and face fusion according to claim 1, it is characterised in that described
Quality evaluation parameter includes size, angle and the definition of human body image or facial image.
A kind of 6. identity recognition device based on gait and face fusion, it is characterised in that including：
Video acquisition and pretreatment module, for gathering video flowing, pedestrian detection and tracking are carried out to video flowing, obtain human body frame
Image sequence, while Face datection is carried out to human body two field picture, obtain human face image sequence corresponding to human body frame image sequence；
Method of Gait Feature Extraction module, Method of Gait Feature Extraction is carried out to the human body image in the human body frame image sequence, and obtained
Quality evaluation parameter, the quality evaluation fraction of the gait feature is calculated according to quality evaluation parameter；
Face characteristic extraction module, face characteristic extraction is carried out to each image in human face image sequence, and obtain facial image
Quality evaluation parameter, according to quality evaluation parameter calculate face characteristic quality evaluation fraction, by quality evaluation fraction highest
Facial image as facial image to be identified；
Identification module, gait feature and face characteristic are weighted according to respective quality score, input SVM classifier is carried out
Identification.
7. a kind of identity recognition device based on gait and face fusion according to claim 6, it is characterised in that described
Video acquisition and pretreatment module carry out pedestrian detection using Faster-RCNN deep learning method to video flowing, adopt simultaneously
With correlation filtering, to detecting that pedestrian is tracked；Face datection is carried out using SSD singles detector, captures face.
8. a kind of identity recognition device based on gait and face fusion according to claim 7, it is characterised in that described
Method of Gait Feature Extraction module is specifically used for：
Size normalization processing is carried out to the human body image in human body frame image sequence；
Human body frame image sequence input gestures after processing are identified into network, extract the posture feature of each human body image, it is raw
Posture characteristic sequence corresponding to adult body frame image sequence, while the quality evaluation parameter of human body two field picture is extracted, and according to matter
Measure the quality evaluation fraction that evaluating calculates human body two field picture；
The posture characteristic sequence is inputted into Gait Recognition network, obtains the characteristic vector for describing body gait.
9. a kind of identity recognition device based on gait and face fusion according to claim 8, it is characterised in that described
Face characteristic extraction module is specifically used for：
Face datection is carried out using SSD singles detector to each facial image in human face image sequence, extracts every people
The 3D Landmark of face, while the quality evaluation parameter of facial image is obtained, and according to the quality evaluation parameter meter of facial image
Calculate the quality evaluation fraction of every facial image；
Choose in human face image sequence that quality evaluation fraction highest facial image is as facial image to be identified, and according to described
The 3D Landmark of facial image to be identified carry out three-dimensional modeling, project front face；
The characteristic vector of facial image to be identified is obtained by face characteristic neutral net.
A kind of 10. other device of identification based on gait and face fusion according to claim 6, it is characterised in that
The quality evaluation parameter includes size, angle and the definition of human body image or facial image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710786656.4A CN107590452A (en) | 2017-09-04 | 2017-09-04 | A kind of personal identification method and device based on gait and face fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710786656.4A CN107590452A (en) | 2017-09-04 | 2017-09-04 | A kind of personal identification method and device based on gait and face fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107590452A true CN107590452A (en) | 2018-01-16 |
Family
ID=61050757
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710786656.4A Pending CN107590452A (en) | 2017-09-04 | 2017-09-04 | A kind of personal identification method and device based on gait and face fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107590452A (en) |
Cited By (51)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108693A (en) * | 2017-12-20 | 2018-06-01 | 深圳市安博臣实业有限公司 | Intelligent identification monitoring device and recognition methods based on 3D high definition VR panoramas |
| CN108460364A (en) * | 2018-03-27 | 2018-08-28 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| CN108629299A (en) * | 2018-04-24 | 2018-10-09 | 武汉幻视智能科技有限公司 | A kind of matched long-time multi-object tracking method of combination face and system |
| CN108734107A (en) * | 2018-04-24 | 2018-11-02 | 武汉幻视智能科技有限公司 | A kind of multi-object tracking method and system based on face |
| CN109086859A (en) * | 2018-08-01 | 2018-12-25 | 上海小蚁科技有限公司 | People flow rate statistical method, apparatus and computer readable storage medium |
| CN109145742A (en) * | 2018-07-19 | 2019-01-04 | 银河水滴科技（北京）有限公司 | A kind of pedestrian recognition method and system |
| CN109325423A (en) * | 2018-08-29 | 2019-02-12 | 安徽超清科技股份有限公司 | A kind of optimization SSD algorithm for pedestrian detection |
| CN109359544A (en) * | 2018-09-19 | 2019-02-19 | 武汉烽火众智数字技术有限责任公司 | A kind of portrait search method and device |
| CN109409297A (en) * | 2018-10-30 | 2019-03-01 | 咪付（广西）网络技术有限公司 | A kind of personal identification method based on binary channels convolutional neural networks |
| CN109446936A (en) * | 2018-10-12 | 2019-03-08 | 银河水滴科技（北京）有限公司 | A kind of personal identification method and device for monitoring scene |
| CN109753917A (en) * | 2018-12-29 | 2019-05-14 | 中国科学院重庆绿色智能技术研究院 | Face quality optimization method, system, computer readable storage medium and equipment |
| CN109754461A (en) * | 2018-12-29 | 2019-05-14 | 深圳云天励飞技术有限公司 | Image processing method and related product |
| CN109920111A (en) * | 2019-03-05 | 2019-06-21 | 杭州电子科技大学 | A kind of access control system of recognition of face and Gait Recognition fusion |
| CN109948890A (en) * | 2019-01-30 | 2019-06-28 | 红云红河烟草(集团)有限责任公司 | Behavior evaluation method and system for workshop machine station operators |
| CN109961213A (en) * | 2019-01-30 | 2019-07-02 | 红云红河烟草(集团)有限责任公司 | Behavior evaluation method and system for workshop maintenance personnel |
| CN110135240A (en) * | 2019-03-27 | 2019-08-16 | 苏州书客贝塔软件科技有限公司 | A kind of pedestrian's analysis intelligent analysis system based on computer vision |
| CN110147712A (en) * | 2019-03-27 | 2019-08-20 | 苏州书客贝塔软件科技有限公司 | A kind of intelligent cloud platform of pedestrian's analysis |
| CN110175587A (en) * | 2019-05-30 | 2019-08-27 | 黄岩 | A kind of video frequency tracking method based on recognition of face and Algorithm for gait recognition |
| CN110175553A (en) * | 2019-05-23 | 2019-08-27 | 银河水滴科技（北京）有限公司 | The method and device of feature database is established based on Gait Recognition and recognition of face |
| CN110276288A (en) * | 2019-06-17 | 2019-09-24 | 北京影谱科技股份有限公司 | A kind of personal identification method and device based on biological characteristic |
| CN110287889A (en) * | 2019-06-26 | 2019-09-27 | 银河水滴科技（北京）有限公司 | A kind of method and device of identification |
| CN110443232A (en) * | 2018-04-12 | 2019-11-12 | 腾讯科技（深圳）有限公司 | Method for processing video frequency and relevant apparatus, image processing method and relevant apparatus |
| CN110569707A (en) * | 2019-06-25 | 2019-12-13 | 深圳和而泰家居在线网络科技有限公司 | identity recognition method and electronic equipment |
| CN110750671A (en) * | 2019-09-05 | 2020-02-04 | 浙江省北大信息技术高等研究院 | Pedestrian retrieval method and device based on massive unstructured features |
| CN110796094A (en) * | 2019-10-30 | 2020-02-14 | 上海商汤智能科技有限公司 | Control method and device based on image recognition, electronic equipment and storage medium |
| CN110807401A (en) * | 2019-10-29 | 2020-02-18 | 腾讯科技（深圳）有限公司 | User identity identification and multi-user card punching method, device, storage medium and equipment |
| CN110874582A (en) * | 2019-11-18 | 2020-03-10 | 浙江大华技术股份有限公司 | Telecommunication fraud determination method and device, storage medium and electronic device |
| CN110895802A (en) * | 2018-08-23 | 2020-03-20 | 杭州海康威视数字技术股份有限公司 | Image processing method and device |
| CN111008993A (en) * | 2019-12-06 | 2020-04-14 | 江西洪都航空工业集团有限责任公司 | Method and device for tracking pedestrian across mirrors |
| CN111368598A (en) * | 2018-12-26 | 2020-07-03 | 上海银晨智能识别科技有限公司 | Human body biological characteristic acquisition system |
| CN111460884A (en) * | 2020-02-09 | 2020-07-28 | 天津博宜特科技有限公司 | Multi-face recognition method based on human body tracking |
| CN111680608A (en) * | 2020-06-03 | 2020-09-18 | 长春博立电子科技有限公司 | Intelligent sports auxiliary training system and training method based on video analysis |
| CN111881732A (en) * | 2020-06-17 | 2020-11-03 | 北京邮电大学 | SVM (support vector machine) -based face quality evaluation method |
| CN111898515A (en) * | 2020-07-24 | 2020-11-06 | 北京灵汐科技有限公司 | Method and device for identifying identity of pedestrian |
| CN111967497A (en) * | 2020-07-15 | 2020-11-20 | 桂林远望智能通信科技有限公司 | Dynamic identity recognition data processing method and system |
| WO2020249025A1 (en) * | 2019-06-12 | 2020-12-17 | 杭州海康威视数字技术股份有限公司 | Identity information determining method and apparatus, and storage medium |
| CN112132157A (en) * | 2020-08-26 | 2020-12-25 | 浙江工业大学 | Raspberry pie-based gait face fusion recognition method |
| CN112270310A (en) * | 2020-11-24 | 2021-01-26 | 上海工程技术大学 | Cross-camera pedestrian multi-target tracking method and device based on deep learning |
| CN112287770A (en) * | 2020-10-10 | 2021-01-29 | 武汉大学 | Face quality sensing method and system for identity recognition |
| CN112364829A (en) * | 2020-11-30 | 2021-02-12 | 北京有竹居网络技术有限公司 | Face recognition method, device, equipment and storage medium |
| CN112488071A (en) * | 2020-12-21 | 2021-03-12 | 重庆紫光华山智安科技有限公司 | Method, device, electronic equipment and storage medium for extracting pedestrian features |
| CN112507781A (en) * | 2020-10-21 | 2021-03-16 | 天津中科智能识别产业技术研究院有限公司 | Multi-dimensional multi-modal group biological feature recognition system and method |
| CN112560720A (en) * | 2020-12-21 | 2021-03-26 | 奥比中光科技集团股份有限公司 | Pedestrian identification method and system |
| CN112733722A (en) * | 2021-01-11 | 2021-04-30 | 深圳力维智联技术有限公司 | Gesture recognition method, device and system and computer readable storage medium |
| CN112784740A (en) * | 2021-01-21 | 2021-05-11 | 上海市公安局刑事侦查总队 | Gait data acquisition and labeling method and application |
| CN113010731A (en) * | 2021-02-22 | 2021-06-22 | 杭州西湖数据智能研究院 | Multimodal video retrieval system |
| CN113591724A (en) * | 2021-08-02 | 2021-11-02 | 浙江大华技术股份有限公司 | Identity recognition method and device based on gait and veins |
| CN113723188A (en) * | 2021-07-28 | 2021-11-30 | 国网浙江省电力有限公司电力科学研究院 | Dress uniform person identity verification method combining face and gait features |
| CN115171347A (en) * | 2022-01-27 | 2022-10-11 | 浙江浩瀚能源科技有限公司 | In-vehicle personnel monitoring and early warning system and method |
| CN117238045A (en) * | 2023-09-18 | 2023-12-15 | 以萨技术股份有限公司 | Object recognition method based on multi-modal characteristics |
| CN118135462A (en) * | 2024-03-29 | 2024-06-04 | 北京积加科技有限公司 | Stranger intrusion detection method and device based on face and gait recognition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050031195A1 (en) * | 2003-08-08 | 2005-02-10 | Microsoft Corporation | System and method for modeling three dimensional objects from a single image |
| CN101404091A (en) * | 2008-11-07 | 2009-04-08 | 重庆邮电大学 | Three-dimensional human face reconstruction method and system based on two-step shape modeling |
| AU2011101355A4 (en) * | 2011-10-20 | 2011-12-08 | Girija Chetty | Biometric person identity verification base on face and gait fusion |
-
2017
- 2017-09-04 CN CN201710786656.4A patent/CN107590452A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050031195A1 (en) * | 2003-08-08 | 2005-02-10 | Microsoft Corporation | System and method for modeling three dimensional objects from a single image |
| CN101404091A (en) * | 2008-11-07 | 2009-04-08 | 重庆邮电大学 | Three-dimensional human face reconstruction method and system based on two-step shape modeling |
| AU2011101355A4 (en) * | 2011-10-20 | 2011-12-08 | Girija Chetty | Biometric person identity verification base on face and gait fusion |
Non-Patent Citations (3)
| Title |
|---|
| XIAOLI ZHOU等: "Feature fusion of side face and gait for video-based human identification", 《PATTERN RECOGNITION》 * |
| 肖宾杰: "基于图像质量加权的D-S证据理论多生物特征融合识别", 《计算机应用》 * |
| 马樱: "基于视频流的步态识别", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (69)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108693B (en) * | 2017-12-20 | 2019-02-19 | 深圳市安博臣实业有限公司 | Intelligent identification monitoring device and recognition methods based on 3D high definition VR panorama |
| CN108108693A (en) * | 2017-12-20 | 2018-06-01 | 深圳市安博臣实业有限公司 | Intelligent identification monitoring device and recognition methods based on 3D high definition VR panoramas |
| CN108460364A (en) * | 2018-03-27 | 2018-08-28 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| CN108460364B (en) * | 2018-03-27 | 2022-03-11 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| CN110443232A (en) * | 2018-04-12 | 2019-11-12 | 腾讯科技（深圳）有限公司 | Method for processing video frequency and relevant apparatus, image processing method and relevant apparatus |
| CN108734107B (en) * | 2018-04-24 | 2021-11-05 | 武汉幻视智能科技有限公司 | Multi-target tracking method and system based on human face |
| CN108629299B (en) * | 2018-04-24 | 2021-11-16 | 武汉幻视智能科技有限公司 | Long-time multi-target tracking method and system combining face matching |
| CN108734107A (en) * | 2018-04-24 | 2018-11-02 | 武汉幻视智能科技有限公司 | A kind of multi-object tracking method and system based on face |
| CN108629299A (en) * | 2018-04-24 | 2018-10-09 | 武汉幻视智能科技有限公司 | A kind of matched long-time multi-object tracking method of combination face and system |
| CN109145742B (en) * | 2018-07-19 | 2021-05-11 | 银河水滴科技(宁波)有限公司 | Pedestrian identification method and system |
| CN109145742A (en) * | 2018-07-19 | 2019-01-04 | 银河水滴科技（北京）有限公司 | A kind of pedestrian recognition method and system |
| CN109086859A (en) * | 2018-08-01 | 2018-12-25 | 上海小蚁科技有限公司 | People flow rate statistical method, apparatus and computer readable storage medium |
| CN110895802A (en) * | 2018-08-23 | 2020-03-20 | 杭州海康威视数字技术股份有限公司 | Image processing method and device |
| CN110895802B (en) * | 2018-08-23 | 2023-09-01 | 杭州海康威视数字技术股份有限公司 | Image processing method and device |
| CN109325423A (en) * | 2018-08-29 | 2019-02-12 | 安徽超清科技股份有限公司 | A kind of optimization SSD algorithm for pedestrian detection |
| CN109325423B (en) * | 2018-08-29 | 2021-09-21 | 安徽超清科技股份有限公司 | SSD (solid State disk) optimization method for pedestrian detection |
| CN109359544B (en) * | 2018-09-19 | 2022-01-21 | 武汉烽火众智数字技术有限责任公司 | Portrait retrieval method and device |
| CN109359544A (en) * | 2018-09-19 | 2019-02-19 | 武汉烽火众智数字技术有限责任公司 | A kind of portrait search method and device |
| CN109446936A (en) * | 2018-10-12 | 2019-03-08 | 银河水滴科技（北京）有限公司 | A kind of personal identification method and device for monitoring scene |
| CN109409297B (en) * | 2018-10-30 | 2021-11-23 | 咪付（广西）网络技术有限公司 | Identity recognition method based on dual-channel convolutional neural network |
| CN109409297A (en) * | 2018-10-30 | 2019-03-01 | 咪付（广西）网络技术有限公司 | A kind of personal identification method based on binary channels convolutional neural networks |
| CN111368598A (en) * | 2018-12-26 | 2020-07-03 | 上海银晨智能识别科技有限公司 | Human body biological characteristic acquisition system |
| CN109754461A (en) * | 2018-12-29 | 2019-05-14 | 深圳云天励飞技术有限公司 | Image processing method and related product |
| CN109753917A (en) * | 2018-12-29 | 2019-05-14 | 中国科学院重庆绿色智能技术研究院 | Face quality optimization method, system, computer readable storage medium and equipment |
| CN109961213A (en) * | 2019-01-30 | 2019-07-02 | 红云红河烟草(集团)有限责任公司 | Behavior evaluation method and system for workshop maintenance personnel |
| CN109948890A (en) * | 2019-01-30 | 2019-06-28 | 红云红河烟草(集团)有限责任公司 | Behavior evaluation method and system for workshop machine station operators |
| CN109920111A (en) * | 2019-03-05 | 2019-06-21 | 杭州电子科技大学 | A kind of access control system of recognition of face and Gait Recognition fusion |
| CN110147712A (en) * | 2019-03-27 | 2019-08-20 | 苏州书客贝塔软件科技有限公司 | A kind of intelligent cloud platform of pedestrian's analysis |
| CN110135240A (en) * | 2019-03-27 | 2019-08-16 | 苏州书客贝塔软件科技有限公司 | A kind of pedestrian's analysis intelligent analysis system based on computer vision |
| CN110175553A (en) * | 2019-05-23 | 2019-08-27 | 银河水滴科技（北京）有限公司 | The method and device of feature database is established based on Gait Recognition and recognition of face |
| CN110175587A (en) * | 2019-05-30 | 2019-08-27 | 黄岩 | A kind of video frequency tracking method based on recognition of face and Algorithm for gait recognition |
| WO2020249025A1 (en) * | 2019-06-12 | 2020-12-17 | 杭州海康威视数字技术股份有限公司 | Identity information determining method and apparatus, and storage medium |
| CN110276288A (en) * | 2019-06-17 | 2019-09-24 | 北京影谱科技股份有限公司 | A kind of personal identification method and device based on biological characteristic |
| CN110569707A (en) * | 2019-06-25 | 2019-12-13 | 深圳和而泰家居在线网络科技有限公司 | identity recognition method and electronic equipment |
| CN110287889A (en) * | 2019-06-26 | 2019-09-27 | 银河水滴科技（北京）有限公司 | A kind of method and device of identification |
| CN110750671B (en) * | 2019-09-05 | 2022-09-02 | 杭州未名信科科技有限公司 | Pedestrian retrieval method and device based on massive unstructured features |
| CN110750671A (en) * | 2019-09-05 | 2020-02-04 | 浙江省北大信息技术高等研究院 | Pedestrian retrieval method and device based on massive unstructured features |
| CN110807401B (en) * | 2019-10-29 | 2024-08-13 | 腾讯科技（深圳）有限公司 | User identity recognition and multi-user card punching method and device, storage medium and equipment |
| CN110807401A (en) * | 2019-10-29 | 2020-02-18 | 腾讯科技（深圳）有限公司 | User identity identification and multi-user card punching method, device, storage medium and equipment |
| CN110796094A (en) * | 2019-10-30 | 2020-02-14 | 上海商汤智能科技有限公司 | Control method and device based on image recognition, electronic equipment and storage medium |
| CN110874582A (en) * | 2019-11-18 | 2020-03-10 | 浙江大华技术股份有限公司 | Telecommunication fraud determination method and device, storage medium and electronic device |
| CN111008993A (en) * | 2019-12-06 | 2020-04-14 | 江西洪都航空工业集团有限责任公司 | Method and device for tracking pedestrian across mirrors |
| CN111460884A (en) * | 2020-02-09 | 2020-07-28 | 天津博宜特科技有限公司 | Multi-face recognition method based on human body tracking |
| CN111680608B (en) * | 2020-06-03 | 2023-08-18 | 长春博立电子科技有限公司 | Intelligent sports auxiliary training system and training method based on video analysis |
| CN111680608A (en) * | 2020-06-03 | 2020-09-18 | 长春博立电子科技有限公司 | Intelligent sports auxiliary training system and training method based on video analysis |
| CN111881732A (en) * | 2020-06-17 | 2020-11-03 | 北京邮电大学 | SVM (support vector machine) -based face quality evaluation method |
| CN111967497B (en) * | 2020-07-15 | 2024-01-26 | 桂林远望智能通信科技有限公司 | Dynamic identification data processing method and system |
| CN111967497A (en) * | 2020-07-15 | 2020-11-20 | 桂林远望智能通信科技有限公司 | Dynamic identity recognition data processing method and system |
| CN111898515A (en) * | 2020-07-24 | 2020-11-06 | 北京灵汐科技有限公司 | Method and device for identifying identity of pedestrian |
| CN112132157A (en) * | 2020-08-26 | 2020-12-25 | 浙江工业大学 | Raspberry pie-based gait face fusion recognition method |
| CN112132157B (en) * | 2020-08-26 | 2024-06-18 | 浙江工业大学 | Gait face fusion recognition method based on raspberry pie |
| CN112287770A (en) * | 2020-10-10 | 2021-01-29 | 武汉大学 | Face quality sensing method and system for identity recognition |
| CN112507781A (en) * | 2020-10-21 | 2021-03-16 | 天津中科智能识别产业技术研究院有限公司 | Multi-dimensional multi-modal group biological feature recognition system and method |
| CN112507781B (en) * | 2020-10-21 | 2023-11-21 | 天津中科智能识别产业技术研究院有限公司 | Multi-dimensional multi-mode group biological feature recognition system and method |
| CN112270310A (en) * | 2020-11-24 | 2021-01-26 | 上海工程技术大学 | Cross-camera pedestrian multi-target tracking method and device based on deep learning |
| CN112364829A (en) * | 2020-11-30 | 2021-02-12 | 北京有竹居网络技术有限公司 | Face recognition method, device, equipment and storage medium |
| CN112488071B (en) * | 2020-12-21 | 2021-10-26 | 重庆紫光华山智安科技有限公司 | Method, device, electronic equipment and storage medium for extracting pedestrian features |
| CN112488071A (en) * | 2020-12-21 | 2021-03-12 | 重庆紫光华山智安科技有限公司 | Method, device, electronic equipment and storage medium for extracting pedestrian features |
| CN112560720A (en) * | 2020-12-21 | 2021-03-26 | 奥比中光科技集团股份有限公司 | Pedestrian identification method and system |
| CN112733722A (en) * | 2021-01-11 | 2021-04-30 | 深圳力维智联技术有限公司 | Gesture recognition method, device and system and computer readable storage medium |
| CN112784740A (en) * | 2021-01-21 | 2021-05-11 | 上海市公安局刑事侦查总队 | Gait data acquisition and labeling method and application |
| CN113010731B (en) * | 2021-02-22 | 2022-05-20 | 杭州西湖数据智能研究院 | Multimodal video retrieval system |
| CN113010731A (en) * | 2021-02-22 | 2021-06-22 | 杭州西湖数据智能研究院 | Multimodal video retrieval system |
| CN113723188A (en) * | 2021-07-28 | 2021-11-30 | 国网浙江省电力有限公司电力科学研究院 | Dress uniform person identity verification method combining face and gait features |
| CN113591724A (en) * | 2021-08-02 | 2021-11-02 | 浙江大华技术股份有限公司 | Identity recognition method and device based on gait and veins |
| CN115171347A (en) * | 2022-01-27 | 2022-10-11 | 浙江浩瀚能源科技有限公司 | In-vehicle personnel monitoring and early warning system and method |
| CN117238045A (en) * | 2023-09-18 | 2023-12-15 | 以萨技术股份有限公司 | Object recognition method based on multi-modal characteristics |
| CN117238045B (en) * | 2023-09-18 | 2024-03-08 | 以萨技术股份有限公司 | Object recognition method based on multi-modal characteristics |
| CN118135462A (en) * | 2024-03-29 | 2024-06-04 | 北京积加科技有限公司 | Stranger intrusion detection method and device based on face and gait recognition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107590452A (en) | A kind of personal identification method and device based on gait and face fusion | |
| CN114067358B (en) | Human body posture recognition method and system based on key point detection technology | |
| CN103942577B (en) | Based on the personal identification method for establishing sample database and composite character certainly in video monitoring | |
| CN106295522B (en) | A kind of two-stage anti-fraud detection method based on multi-orientation Face and environmental information | |
| CN109522853B (en) | Face datection and searching method towards monitor video | |
| CN107423730A (en) | A kind of body gait behavior active detecting identifying system and method folded based on semanteme | |
| CN104200200B (en) | Fusion depth information and half-tone information realize the system and method for Gait Recognition | |
| Ahmed et al. | Kinect-based gait recognition using sequences of the most relevant joint relative angles | |
| Denman et al. | Soft-biometrics: unconstrained authentication in a surveillance environment | |
| CN103718175A (en) | Apparatus, method, and medium detecting object pose | |
| Pandey et al. | Hand gesture recognition for sign language recognition: A review | |
| CN105740779A (en) | Method and device for human face in-vivo detection | |
| CN103996033B (en) | Human identification method based on tracking of human face five-sense-organ coordinates | |
| CN106295544A (en) | A kind of unchanged view angle gait recognition method based on Kinect | |
| Huang et al. | Hand vein recognition based on oriented gradient maps and local feature matching | |
| Kusakunniran et al. | Automatic gait recognition using weighted binary pattern on video | |
| Reddy et al. | Ocularnet: deep patch-based ocular biometric recognition | |
| CN105320948A (en) | Image based gender identification method, apparatus and system | |
| Sun et al. | Human recognition for following robots with a Kinect sensor | |
| CN107798308A (en) | A kind of face identification method based on short-sighted frequency coaching method | |
| CN110020627A (en) | A kind of pedestrian detection method based on depth map and Fusion Features | |
| CN102184016A (en) | Noncontact type mouse control method based on video sequence recognition | |
| Aung et al. | Gait biometric-based human recognition system using deep convolutional neural network in surveillance system | |
| CN105550642B (en) | Gender identification method and system based on multiple dimensioned linear Differential Characteristics low-rank representation | |
| CN104573628A (en) | Three-dimensional face recognition method |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180116 |