Hyperspectral face recognition with minimum noise fraction, histogram of oriented gradient features and collaborative representation-based classifier

Hyperspectral imaging provides new opportunities for improving face recognition accuracy. However, it poses such challenges as difficulty in data acquisition, low signal to noise ratio (SNR), and high dimensionality. In this paper, we propose a novel method for hyperspectral face recognition with good recognition rates. We first reduce noise adaptively from each spectral band and then crop each face. We perform minimum noise fraction (MNF) transform to the cropped face data cube in order to extract a number of MNF bands. We extract histogram of oriented gradients (HOG) features from each MNF band. We conducted some experiments to test this new method for hyperspectral face recognition with very promising results. For Hong Kong Polytechnic University Hyperspectral Face Database (PolyU-HSFD), we achieved an average correct recognition rate of 95.4% with standard deviation of 2.6 (95.4% ±2.6). For CMU Hyperspectral Face Database (CMU-HSFD), we achieved an average correct recognition rate of 98.1% with standard deviation of 0.8 (98.1% ±0.8). The reasons why we choose MNF for hyperspectral face recognition are because it can separate noise from fine features in the face data cube and at the same time reduce the dimensionality of the face data cube. In this way, our proposed face recognition method will be faster than those methods without dimensionality reduction.