Partial least squares regression on DCT domain for infrared face recognition

Zhihua Xie

doi:10.1117/12.2068214

17 September 2014 Partial least squares regression on DCT domain for infrared face recognition

Zhihua Xie

Proceedings Volume 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014); 92301I (2014) https://doi.org/10.1117/12.2068214
Event: Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 2014, Wuhan, China

Abstract

Compact and discriminative feature extraction is a challenging task for infrared face recognition. In this paper, we propose an infrared face recognition method using Partial Least Square (PLS) regression on Discrete Cosine Transform (DCT) coefficients. With the strong ability for data de-correlation and compact energy, DCT is studied to get the compact features in infrared face. To dig out discriminative information in DCT coefficients, class-specific One-to-Rest Partial Least Squares (PLS) classifier is learned for accurate classification. The infrared data were collected by an infrared camera Thermo Vision A40 supplied by FLIR Systems Inc. The experimental results show that the recognition rate of the proposed algorithm can reach 95.8%, outperforms that of the state of art infrared face recognition methods based on Linear Discriminant Analysis (LDA) and DCT.

Citation Download Citation

Zhihua Xie "Partial least squares regression on DCT domain for infrared face recognition", Proc. SPIE 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 92301I (17 September 2014); https://doi.org/10.1117/12.2068214

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