A novel framework for face recognition using robust local representation–based classification
Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face reco...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719836082 |
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| _version_ | 1832545248698433536 |
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| author | Aihua Yu Gang Li Beiping Hou Hongan Wang Gaoya Zhou |
| author_facet | Aihua Yu Gang Li Beiping Hou Hongan Wang Gaoya Zhou |
| author_sort | Aihua Yu |
| collection | DOAJ |
| description | Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems. |
| format | Article |
| id | doaj-art-989276065d0b4585a2a76e41f290a667 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-989276065d0b4585a2a76e41f290a6672025-02-03T07:26:22ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-03-011510.1177/1550147719836082A novel framework for face recognition using robust local representation–based classificationAihua Yu0Gang Li1Beiping Hou2Hongan Wang3Gaoya Zhou4School of Automation and Electrical Engineering, Zhejiang University of Science and Technology (ZUST), Hangzhou, P.R. ChinaAdvanced Institute of Information Technology (AIIT), Peking University, Hangzhou, P.R. ChinaSchool of Automation and Electrical Engineering, Zhejiang University of Science and Technology (ZUST), Hangzhou, P.R. ChinaSchool of Automation and Electrical Engineering, Zhejiang University of Science and Technology (ZUST), Hangzhou, P.R. ChinaSchool of Automation and Electrical Engineering, Zhejiang University of Science and Technology (ZUST), Hangzhou, P.R. ChinaFace recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems.https://doi.org/10.1177/1550147719836082 |
| spellingShingle | Aihua Yu Gang Li Beiping Hou Hongan Wang Gaoya Zhou A novel framework for face recognition using robust local representation–based classification International Journal of Distributed Sensor Networks |
| title | A novel framework for face recognition using robust local representation–based classification |
| title_full | A novel framework for face recognition using robust local representation–based classification |
| title_fullStr | A novel framework for face recognition using robust local representation–based classification |
| title_full_unstemmed | A novel framework for face recognition using robust local representation–based classification |
| title_short | A novel framework for face recognition using robust local representation–based classification |
| title_sort | novel framework for face recognition using robust local representation based classification |
| url | https://doi.org/10.1177/1550147719836082 |
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