1 March 2013 Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis
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Abstract
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.
© 2013 SPIE and IS&T
Sajad Farokhi, Sajad Farokhi, Siti Mariyam Shamsuddin, Siti Mariyam Shamsuddin, Jan Flusser, Jan Flusser, Usman Ullah Sheikh, Usman Ullah Sheikh, Mohammad Khansari, Mohammad Khansari, Kourosh Jafari-Khouzani, Kourosh Jafari-Khouzani, } "Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis," Journal of Electronic Imaging 22(1), 013030 (1 March 2013). https://doi.org/10.1117/1.JEI.22.1.013030 . Submission:
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