Hyperspectral face recognition with a spatial information fusion for local dynamic texture patterns and collaborative representation 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, while offering excellent opportunities, it also brings new challenges, such as low signal-to-noise ratio, interband misalignment, and high data dimensionality. Based on these ad hoc problems, literature has already proposed some optimisation methods including dimensionality reduction, image denoising, and alignment to perform face recognition, yet lacking comprehensive evaluation. This paper proposes a novel hyperspectral face recognition algorithm that is based on spatial information fusion for feature extraction (histogram of local dynamic texture patterns) and collaborative representation classifier for classification. Meanwhile, the algorithm is applied to three popular hyperspectral face databases, Carnegie Mellon University (CMU)-hyperspectral face database (HSFD), University of Western Australia (UWA)-HSFD, and Hong Kong Polytechnic University (PolyU)-HSFD databases. Experimental results demonstrate that CMU-HSFD and UWA-HSFD databases achieve very competitive classification results. PolyU-HSFD database also achieves rather good classification rates. The best recognition results are 98.5% ± 0.95, 96.6% ± 0.98, and 94.0% ± 2.86 for CMU-HSFD, UWA-HSFD and PolyU-HSFD, respectively. It demonstrates experimentally that this algorithm can be used to recognise faces. Moreover, we compared eight existing state-of-the-art face recognition techniques with our proposed method in performing hyperspectral face recognition. In this research, we formulate hyperspectral face recognition as an image-set classification problem and evaluate the performances compared with other kinds of algorithms. Comparisons with the eight existing hyperspectral face recognition techniques on three standard datasets show that the proposed algorithm outperforms most other state-of-the-art algorithms, indicating that it is a promising approach for hyperspectral face recognition.