Abstract
Infrared face imaging, being light- independent, and not vulnerable to facial skin expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. However, to obtain the compact and discriminative feature extracted from infrared face image is a challenging task. In this essay, infrared face recognition method using Discrete Cosine Transform (DCT) and Partial Least Square (PLS) is proposed. Due to strong ability for data de-correlation and compact energy, DCT is studied to obtain the compact features in infrared face. To make full use of the discriminative information in DCT coefficients, the final classifier formulates PLS regression for accurate classification. The experimental results show that the proposed algorithm outperforms Principle Component Analysis (PCA) and DCT based infrared face recognition algorithms.KeywordsInfrared face recognitionPartial least squarefeature extractiondiscrete cosine transform
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