Abstract
A feature extraction algorithm of near-infrared human face based on wavelet transform and 2DPCA (two-dimension principal component analysis) was proposed. This algorithm firstly applied wavelet transform to the near-infrared face images, obtained the low frequency components of face images, and removed high frequency components. Secondly applied 2DPCA to the low frequency components of the face images for feature extraction. Finally completed the face recognition using Euclidean distance. The experimental results based on near-infrared face database clearly showed that the proposed algorithm could get higher recognition rate than the traditional PCA and 2DPCA algorithm, which demonstrated the efficiency of the proposed method.
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