Combining Pixel Selection with Covariance Similarity Approach in Hyperspectral Face Recognition

Rich spectral information of hyperspectral images provides a non-invasive way to characterize the skin tissues and thereby improves hyperspectral face recognition accuracy. However, the increased computational complexity is reduced by efficient feature selection method. In this paper, we amalgamate pixel selection with spectral discrimination. The pixel selection process choses the informative pixels, which improves the computational performance, whereas, covariance similarity encompasses the complete spectral information. We compare the covariance matrices formed from the selected pixels obtained by fiducial points and edge. A detailed study of the covariance similarity measures has been conducted. This leads us to use Jeffrey's KL divergence measure because of its tighter bounds and better noise robustness. We have evaluated our proposed framework on two popular hyperspectral face recognition datasets.