Abstract
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.
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References
Zhang B, Zhang L, Zhang D, Shen L (2010) Directional binary code with application to PolyU near-infrared face database. Pattern Recog Lett 31:2337–2344
Li SZ, Chu R, Liao S, Zhang L (2007) Illumination invariant face recognition using near-infrared images. IEEE Trans Pattern Anal Mach Intell 29:627–639
Flusser J, Suk T, Zitova B (2009) Moments and moment invariants in pattern recognition. Wiley, Chichester
Haddadnia J, Ahmadi M, Faez K (2002) An efficient method for recognition of human faces using higher orders pseudo Zernike moment invariant. In: Fifth IEEE international conference on automatic face and gesture recognition. IEEE Press, Washington DC, pp 330–335
Zhi R, Ruan Q (2008) A comparative study on region-based moments for facial expression recognition. In: Congress on image and signal processing. IEEE Press, Sanya, pp 600–604
Lajevardi SM, Hussain ZM (2010) Higher order orthogonal moments for invariant facial expression recognition. Digit Signal Process 20:1771–1779
Farokhi S, Shamsuddin SM, Flusser J, Sheikh UU (2012) Assessment of time-lapse in visible and thermal face recognition. Int J Comput Commun Eng 6:181–186
Farokhi S, Shamsuddin SM, Flusser J, Sheikh UU, Khansari M, Kourosh J-K (2013) Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis. J Electron Imaging 22:013030
Broumandnia A, Shanbehzadeh J (2007) Fast Zernike wavelet moments for Farsi character recognition. Image Vision Comput 25:717–726
Ye J, Wang T (2006) Regularized discriminant analysis for high dimensional, low sample size data. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, Philadelphia, pp 454–463
Acknowledgments
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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Farokhi, S., Shamsuddin, S.M., Sheikh, U.U., Flusser, J. (2014). Near Infrared Face Recognition: A Comparison of Moment-Based Approaches. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_15
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DOI: https://doi.org/10.1007/978-981-4585-42-2_15
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