Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions

Chen Li, Shuai Zhao, Ke Xiao and Yanjie Wang
Volume: 14, No: 1, Page: 191 ~ 204, Year: 2018
10.3745/JIPS.04.0060
Keywords: Deep Belief Network, Enhanced Local Texture Feature, Face Recognition, Illumination Variation
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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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Cite this article
IEEE Style
Chen Li, Shuai Zhao, Ke Xiao, and Yanjie Wang , "Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions," Journal of Information Processing Systems, vol. 14, no. 1, pp. 191~204, 2018. DOI: 10.3745/JIPS.04.0060.

ACM Style
Chen Li, Shuai Zhao, Ke Xiao, and Yanjie Wang , "Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions," Journal of Information Processing Systems, 14, 1, (2018), 191~204. DOI: 10.3745/JIPS.04.0060.