Face recognition algorithm based on attractive local second gradient contours
In order to solve the problem that traditional LDP algorithms is difficult to balance the effectiveness of feature extraction and the stability of feature encoding, an attractive local second gradient contours (ALSGC) face feature extraction algorithm was proposed.Firstly, the Kirsch operator was us...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2021-07-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021140/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841528964460314624 |
|---|---|
| author | Xueyi YE Dingwei QIAN Na YING Tao WANG |
| author_facet | Xueyi YE Dingwei QIAN Na YING Tao WANG |
| author_sort | Xueyi YE |
| collection | DOAJ |
| description | In order to solve the problem that traditional LDP algorithms is difficult to balance the effectiveness of feature extraction and the stability of feature encoding, an attractive local second gradient contours (ALSGC) face feature extraction algorithm was proposed.Firstly, the Kirsch operator was used to calculate the neighborhood edge response image of the face image.Secondly, an attraction descriptor was introduced and the local average gray value and global average gray value of the edge response image and the neighborhood center gray value was combined to complete the local attraction pattern encoding.The entire image was traversed to get the ALSGC face feature map, the ALSGC feature map was divided to blocks and the statistical histograms of different patterns for each block was obtained by calculation.Finally, the statistical histograms of all the blocks were cascaded to generate the corresponding feature vector and support vector machine was used to complete classification and recognition.The proposed algorithm not only overcame the insufficient effectiveness of LBP, LDP, LDN and other algorithms who extract first-gradient features, but also reduced the sensitivity to changes in expression, posture, occlusion, lighting and random noise of methods such as DLDP, CSLDP, GCSLDP who extracting second-gradient features.It better achieved the balance between the effectiveness of feature extraction and the stability of feature encoding, and took into account the recognition rate and robustness. |
| format | Article |
| id | doaj-art-c05bd6b0b1db4e6a8f9b6f3ae059cbe0 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-07-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-c05bd6b0b1db4e6a8f9b6f3ae059cbe02025-01-15T03:32:27ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-07-01379610659814197Face recognition algorithm based on attractive local second gradient contoursXueyi YEDingwei QIANNa YINGTao WANGIn order to solve the problem that traditional LDP algorithms is difficult to balance the effectiveness of feature extraction and the stability of feature encoding, an attractive local second gradient contours (ALSGC) face feature extraction algorithm was proposed.Firstly, the Kirsch operator was used to calculate the neighborhood edge response image of the face image.Secondly, an attraction descriptor was introduced and the local average gray value and global average gray value of the edge response image and the neighborhood center gray value was combined to complete the local attraction pattern encoding.The entire image was traversed to get the ALSGC face feature map, the ALSGC feature map was divided to blocks and the statistical histograms of different patterns for each block was obtained by calculation.Finally, the statistical histograms of all the blocks were cascaded to generate the corresponding feature vector and support vector machine was used to complete classification and recognition.The proposed algorithm not only overcame the insufficient effectiveness of LBP, LDP, LDN and other algorithms who extract first-gradient features, but also reduced the sensitivity to changes in expression, posture, occlusion, lighting and random noise of methods such as DLDP, CSLDP, GCSLDP who extracting second-gradient features.It better achieved the balance between the effectiveness of feature extraction and the stability of feature encoding, and took into account the recognition rate and robustness.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021140/local attractive patternsecond gradient contoursKirsch operatorfeature extractionface recognition |
| spellingShingle | Xueyi YE Dingwei QIAN Na YING Tao WANG Face recognition algorithm based on attractive local second gradient contours Dianxin kexue local attractive pattern second gradient contours Kirsch operator feature extraction face recognition |
| title | Face recognition algorithm based on attractive local second gradient contours |
| title_full | Face recognition algorithm based on attractive local second gradient contours |
| title_fullStr | Face recognition algorithm based on attractive local second gradient contours |
| title_full_unstemmed | Face recognition algorithm based on attractive local second gradient contours |
| title_short | Face recognition algorithm based on attractive local second gradient contours |
| title_sort | face recognition algorithm based on attractive local second gradient contours |
| topic | local attractive pattern second gradient contours Kirsch operator feature extraction face recognition |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021140/ |
| work_keys_str_mv | AT xueyiye facerecognitionalgorithmbasedonattractivelocalsecondgradientcontours AT dingweiqian facerecognitionalgorithmbasedonattractivelocalsecondgradientcontours AT naying facerecognitionalgorithmbasedonattractivelocalsecondgradientcontours AT taowang facerecognitionalgorithmbasedonattractivelocalsecondgradientcontours |