Joint Feature Learning for Face Recognition
This paper presents a new joint feature learning (JFL) approach to automatically learn feature representation from raw pixels for face recognition. Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for fa...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81670 http://hdl.handle.net/10220/40924 |
| _version_ | 1824454062552121344 |
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| author | Lu, Jiwen Liong, Venice Erin Wang, Gang Moulin, Pierre |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lu, Jiwen Liong, Venice Erin Wang, Gang Moulin, Pierre |
| author_sort | Lu, Jiwen |
| collection | NTU |
| description | This paper presents a new joint feature learning (JFL) approach to automatically learn feature representation from raw pixels for face recognition. Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for face representation, we propose an unsupervised feature learning method to learn hierarchical feature representation. Since different face regions have different physical characteristics, we propose to use different feature dictionaries to represent them, and to learn multiple yet related feature projection matrices for these regions simultaneously. Hence position-specific discriminative information can be exploited for face representation. Having learned these feature projections for different face regions, we perform spatial pooling for face patches within each region to enhance the representative power of the learned features. Moreover, we stack our JFL model into a deep architecture to exploit hierarchical information for feature representation and further improve the recognition performance. Experimental results on five widely used face data sets show the effectiveness of our proposed approach. |
| first_indexed | 2025-02-19T03:16:20Z |
| format | Journal Article |
| id | ntu-10356/81670 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:16:20Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/816702020-03-07T13:56:08Z Joint Feature Learning for Face Recognition Lu, Jiwen Liong, Venice Erin Wang, Gang Moulin, Pierre School of Electrical and Electronic Engineering Face recognition Feature learning This paper presents a new joint feature learning (JFL) approach to automatically learn feature representation from raw pixels for face recognition. Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for face representation, we propose an unsupervised feature learning method to learn hierarchical feature representation. Since different face regions have different physical characteristics, we propose to use different feature dictionaries to represent them, and to learn multiple yet related feature projection matrices for these regions simultaneously. Hence position-specific discriminative information can be exploited for face representation. Having learned these feature projections for different face regions, we perform spatial pooling for face patches within each region to enhance the representative power of the learned features. Moreover, we stack our JFL model into a deep architecture to exploit hierarchical information for feature representation and further improve the recognition performance. Experimental results on five widely used face data sets show the effectiveness of our proposed approach. ASTAR (Agency for Sci., Tech. and Research, S’pore) 2016-07-13T02:33:34Z 2019-12-06T14:35:51Z 2016-07-13T02:33:34Z 2019-12-06T14:35:51Z 2015 Journal Article Lu, J., Liong, V. E., Wang, G., & Moulin, P. (2015). Joint Feature Learning for Face Recognition. IEEE Transactions on Information Forensics and Security, 10(7), 1371-1383. 1556-6013 https://hdl.handle.net/10356/81670 http://hdl.handle.net/10220/40924 10.1109/TIFS.2015.2408431 en IEEE Transactions on Information Forensics and Security © 2015 IEEE. |
| spellingShingle | Face recognition Feature learning Lu, Jiwen Liong, Venice Erin Wang, Gang Moulin, Pierre Joint Feature Learning for Face Recognition |
| title | Joint Feature Learning for Face Recognition |
| title_full | Joint Feature Learning for Face Recognition |
| title_fullStr | Joint Feature Learning for Face Recognition |
| title_full_unstemmed | Joint Feature Learning for Face Recognition |
| title_short | Joint Feature Learning for Face Recognition |
| title_sort | joint feature learning for face recognition |
| topic | Face recognition Feature learning |
| url | https://hdl.handle.net/10356/81670 http://hdl.handle.net/10220/40924 |
| work_keys_str_mv | AT lujiwen jointfeaturelearningforfacerecognition AT liongveniceerin jointfeaturelearningforfacerecognition AT wanggang jointfeaturelearningforfacerecognition AT moulinpierre jointfeaturelearningforfacerecognition |