JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions |
| 영문제목(English Title) |
Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions |
| 저자(Author) |
Chen Li
Shuai Zhao
Ke Xiao
Yanjie Wang
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| 원문수록처(Citation) |
VOL 14 NO. 01 PP. 0191 ~ 0204 (2018. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.
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| 키워드(Keyword) |
Deep Belief Network
Enhanced Local Texture Feature
Face Recognition
Illumination Variation
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| 파일첨부 |
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