Combination of LBP and ESRC for single sample infrared and visible face fusion recognition

Zhihua Xie

doi:10.1117/12.2500381

29 October 2018 Combination of LBP and ESRC for single sample infrared and visible face fusion recognition

Zhihua Xie

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 1083617 (2018) https://doi.org/10.1117/12.2500381
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

Near infrared and visible fusion recognition is an active topic for robust face recognition. Local binary patterns (LBP) based descriptors and sparse representation based classification (SRC) become two significant techniques in face recognition. In this paper, near infrared and visible face fusion recognition based on LBP and extended SRC is proposed for single sample problem. Firstly, the local features are extracted by LBP descriptor for infrared and visible face representation. Secondly, the extend SRC (ESRC) is applied for single sample problem. Finally, to get a robust and time-efficient fusion model for unconstrained face recognition with single sample situation, the infrared and visible features fusion problem is resolved by error-level fusion based on ESRC. Experiments are performed on HITSZ LAB2 database and the experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition with single sample situation.

Citation Download Citation

Zhihua Xie "Combination of LBP and ESRC for single sample infrared and visible face fusion recognition", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083617 (29 October 2018); https://doi.org/10.1117/12.2500381

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