8 March 2018 Near infrared and visible face recognition based on decision fusion of LBP and DCT features
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091H (2018) https://doi.org/10.1117/12.2287099
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.
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Zhihua Xie, Zhihua Xie, Shuai Zhang, Shuai Zhang, Guodong Liu, Guodong Liu, Jinquan Xiong, Jinquan Xiong, } "Near infrared and visible face recognition based on decision fusion of LBP and DCT features", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091H (8 March 2018); doi: 10.1117/12.2287099; https://doi.org/10.1117/12.2287099


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