Infrared Face Recognition based on Multiwavelet Transform and PCA

To extract the discriminative information from the sparse representation of infrared face, infrared face recognition method combining multiwavelet transform and principal component analysis (PCA) is proposed in this paper. Firstly, the effective information in infrared face is represented by multi-wavelet transformation. Then, to integrate more useful information to infrared face recognition, we assign the corresponding weights to different sub-bands in multi-wavelet domain. Finally, based on the weighted fusion distance, the 1-NN classifier is applied to get final recognition result. The experiment results show that the recognition performance of sparse representation based on multi-wavelet representation outperforms that of method based on usual wavelet representation; and the proposed infrared face method considering the useful information in different sub-bands of multiwavelet has better recognition performance, compared with the method based on approximate sub-band.