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Proceedings Paper

Combination of LBP and ESRC for single sample infrared and visible face fusion recognition
Author(s): Zhihua Xie
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Paper 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.

Paper Details

Date Published: 29 October 2018
PDF: 6 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083617 (29 October 2018); doi: 10.1117/12.2500381
Show Author Affiliations
Zhihua Xie, Jiangxi Science and Technology Normal Univ. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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