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

Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, due to the high dimensionality of hyperspectral data, discriminative feature extraction for face recognition is more challenging than 2D images. For dimensionality reduction and feature extraction most of the previous approaches just sub sampled the hyperspectral data [5, 6, 9] or used simple PCA [3]. In contrast, we propose the three dimensional Discrete Cosine Transform (3D-DCT) for feature extraction (Fig. 1). Exploiting the fact that hyperspectral data is usually highly correlated in the spatial and spectral dimensions, a transform such as DCT is expected to perform information compaction in a few coefficients by providing maximal decorrelation. DCT transform being an approximation of the KL-Transformation optimally compacts the signal information in a given number of transform coefficients. Moreover, compared to other transforms, such as the Fourier transform, the transformed coefficients are real and thus require less data to process. The Discrete Cosine Transform (DCT) [1] expresses a discrete signal, such as a 2D image or a hyperspectral cube, as a linear combination of mutually uncorrelated cosine basis functions [4]. DCT generates a compact energy spectrum of the signal where the low-frequency coefficients encode most of the signal information. A compact signal representation can be obtained by selecting only the low-frequency coefficient as features. The 2D-DCT of a 2D image h(x,y)N1×N2 , and the 3D-DCT of a hyperspectral cube H(x,y,λ )N1×N2×N3 are given by