Abstract
Face image with illumination variation usually contains redundant data that will seriously reduce the recognition rate. To combat the influence of illumination variation and extracting illumination-robust feature, a novel feature extraction method is proposed. The novel method is based on the combination of Center-Symmetric Local Binary Pattern (CS-LBP) and the fusion of the vertical and horizontal component images derived from wavelet decomposition. Numerous experiments have been done on the Extended Yale B to verify its effectiveness. The experimental results show that by applying the proposed method, redundant data caused by severe illumination variation can be filtered, while useful texture information can be reserved and enhanced. Compared with CSLBP, it significantly improves the face recognition performance under severe illumination variation.
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Acknowledgment
This research is supported by National Natural Foundation of China (Grant No. 61503005), Beijing Natural Science Foundation (4162022), High Innovation Program of Beijing (2015000026833ZK04), Science and Technology Innovation Service Ability Construction (2017).
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Li, C., Zhao, S., Xiao, K., Wang, Y. (2017). Face Recognition Based on Enhanced CSLBP. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_86
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DOI: https://doi.org/10.1007/978-981-10-5041-1_86
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