Abstract
In order to eliminate the effect of illumination variations, in this paper, we propose a novel face recognition algorithm based on Nonsubsampled contourlet transform (NSCT) and NeighShrink denoise model. NSCT is a fully shift-invariant, multi-scale, and multi-direction transform, which can better preserve edges. Combined with NeighShrink denoise techniques that considers the correlation of neighboring contourlet transform coefficients, NSCT can represent illumination invariant more completely. Experimental results on the Yale B and CMU PIE face databases show that the proposed method achieves satisfactory recognition rates under varying illumination conditions.
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Ma, Y., Xia, S., Zheng, G., Ma, X. (2012). Illumination Invariant Face Recognition Based on Nonsubsampled Contourlet Transform and NeighShrink Denoise. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_45
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DOI: https://doi.org/10.1007/978-3-642-35286-7_45
Publisher Name: Springer, Berlin, Heidelberg
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