Near Infrared Face Recognition: A Comparison of Moment-Based Approaches
Moment based methods have evolved into a powerful tool for face recognition applications. In this paper, a comparative study on moments based feature extraction methods in terms of their capability to recognize facial images with different challenges is done to evaluate the performance of different type of moments. The moments include Geometric moments (GM’s), Zernike moments (ZM’s), Pseudo-Zernike moments (PZM’s) and Wavelet moments (WM’s). Experiments conducted on CASIA NIR database showed that Zernike moments outperformed other moment-based methods for facial images with different challenges such as facial expressions, head pose and noise.
KeywordsMoments Near infrared Comparative study Face recognition
The authors would like to thank Universiti Teknologi Malaysia (UTM) for the support in Research and Development, and Soft Computing Research Group (SCRG) for the inspiration in making this study a success, the Institute of Automation, Chinese Academy of Sciences (CASIA) for providing CASIA NIR database to carry out this experiment and Institute of Information Theory and Automation (UTIA) for providing MATLAB codes. This work is supported by the Ministry of Higher Education (MOHE) under Long Term Research Grant Scheme (LRGS/TD/2011/UTM/ICT/03- 4L805) and the Research Grant No (Q.J130000.2623.08J89). It is also partially supported by the Czech Science Foundation under the grant No. P103/11/1552.
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