UZAIR, MAHMOOD, MIAN: HYPERSPECTRAL FACE RECOGNITION USING 3D DCT 1 Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares
نویسندگان
چکیده
Hyperspectral imaging offers new opportunities for inter-person facial discrimination. 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 represent each hyperspectral facial cube by a small number of low frequency DCT coefficients and formulate Partial Least Square (PLS) regression for accurate classification. The proposed algorithm is evaluated on three standard hyperspectral face databases. Experimental results show that the proposed algorithm outperforms five current state of the art hyperspectral face recognition algorithms by a significant margin.
منابع مشابه
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]...
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