Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis

0390234 - ÚTIA 2014 RIV US eng J - Journal Article
Farokhi, S. - Shamsuddin, S. M. - Flusser, Jan - Sheikh, U. U. - Khansari, M. - Jafari-Khouzani, K.
Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis.
Journal of Electronic Imaging. Roč. 22, č. 1 (2013), s. 1-11. ISSN 1017-9909. E-ISSN 1560-229X
R&D Projects: GA ČR GAP103/11/1552
Keywords : face recognition * infrared imaging * image moments
Subject RIV: JD - Computer Applications, Robotics
Impact factor: 0.850, year: 2013
http://library.utia.cas.cz/separaty/2013/ZOI/flusser-rotation and noise invariant near-infrared face recognition by means of zernike moments and spectral regression discriminant analysis.pdf

Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing.We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the “small sample size” problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis.
Permanent Link: http://hdl.handle.net/11104/0219539