Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares

Hyperspectral imaging offers new opportunities for inter-person facial discrimina-tion. However, compact and discriminative feature extraction from high dimensional hyperspectral image cubes is a challenging task. We propose a spatio-spectral feature extraction method based on the 3D Discrete Cosine Transform (3D-DCT). The 3D-DCT optimally compacts information in the low frequency coefficients. Therefore, we rep-resent each hyperspectral facial cube by a small number of low frequency DCT coef-ficients and formulate Partial Least Square (PLS) regression for accurate classification. The proposed algorithm is evaluated on three standard hyperspectral face databases. Ex-perimental results show that the proposed algorithm outperforms five current state of the art hyperspectral face recognition algorithms by a significant margin. 1