Hyperspectral face recognition via feature extraction and CRC‐based classifier

Abstract

Hyperspectral face recognition provides improved classification rates due to its abundant information in the face cubes of every subject in hyperspectral face databases. However, it is less popular in face recognition due to its difficulty in data acquisition, low signal-to-noise ratio, and high dimensionality. The authors compare five existing descriptors that are frequently used in 2D face recognition, and use collaborative representation classifier (CRC) with two voting techniques for hyperspectral face recognition. Experimental results demonstrate that, for PolyU-HSFD database, Gabor filter bank-based features are very robust to both Gaussian white noise and shot noise, and it achieves very competitive classification results. For CMU-HSFD database, when the noise level is low, histogram of oriented gradients (HOG) yields good classification results. In addition, when the noise level is high, raw facial images without feature extraction perform very well in term of correct classification rate. The local binary pattern and HOG descriptor are very sensitive to noise even though they achieve rather good classification rates if the facial images contain no noise. The best recognition result for the PolyU-HSFD is 96.4% ± 2.3 and for the CMU-HSFD is 98.0% ± 0.7.