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Proceedings Paper

Partial least squares regression on DCT domain for infrared face recognition
Author(s): Zhihua Xie
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Paper 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.

Paper Details

Date Published: 17 September 2014
PDF: 6 pages
Proc. SPIE 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 92301I (17 September 2014); doi: 10.1117/12.2068214
Show Author Affiliations
Zhihua Xie, Jiangxi Science and Technology Normal Univ. (China)


Published in SPIE Proceedings Vol. 9230:
Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014)
Qingming Luo; Lihong V. Wang; Valery V. Tuchin, Editor(s)

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