Illumination Invariant Extracting Algorithm Based on Nonsubsampled Contourlet Transform

Face recognition, a non-contact and friendly biometric identification technology, has broad application prospects in the military, public security and economic security. Related research in recent years has made great progress, a number of excellent face recognition algorithms have emerged, and a number of face recognition systems have achieved good performance. However, many issues still remain to be addressed and illumination changes remain one of the major challenges for current face recognition systems. The report of FRVT 2006 shows that varying illumination will seriously affect the performance of face recognition. In order to eliminate the effect of varying illumination on face recognition, a novel illumination invariant method based on nonsubsampled Contourlet transform is proposed. Firstly, we perform illumination normalization on images under varying illumination, which can reduce the effect of varying illumination to some extent. Secondly, the logarithmic transformation and the nonsubsampled Contourlet transform is used to decompose the images into its low frequency and high frequency directional subband components. Thirdly, adaptive NormalShrink is applied to each directional subband to eliminate noise, and the histogram equalization is applied to the low frequency components aimed at weakening the illumination affects further. Lastly, the illumination invariant is obtained by inverse nonsubsampled Contourlet transform using the modified low frequency components and high frequency directional subband components. Experimental results on the Yale face database B and CMU PIE database show that the proposed method can effectively eliminate the effect of varying illumination on face recognition and the obtained illumination invariant is robust.