融合NSCT和自适应平滑的光照不变量提取算法

Extraction of Illumination Invariants Using NSCT and Adaptive Smoothing

为了更好地解决光照变化对人脸识别系统的干扰问题,提出一种融合了NSCT和自适应平滑的算法,以提取带有更多人脸结构信息的光照不变量.首先用NSCT分解对数域人脸图像,并对各高频子带进行NormalShrink阈值滤波;再将滤波后的高频子带和未经处理的低频子带进行逆NSCT处理得到人脸图像的模糊图像;然后对NSCT分解后的低频子带使用自适应平滑提取出低频子带中的人脸细节信息;最后结合该人脸细节信息和模糊图像进行计算,得到人脸图像的光照不变量.该不变量有效地弥补了NSCT方法中缺乏低频子带中的人脸细节信息的不足,提高了人脸信息的利用率.在Yale B和CMU PIE人脸库上的实验结果表明,该算法能够有效地消除光照变化的影响,具有更优的人脸识别性能,提高人脸识别系统的光照鲁棒性.

To better handle light changes in face recognition ,a method combining NSCT and adaptive smoothing is proposed ,to extract illumination invariants with more structural information of faces . T he method first decomposes a face image in the logarithmic domain , then applies NormalShrink filtering to each high‐frequency subband .Next ,a blurred face image is obtained by performing inverse NSCT on the filtered high‐frequency and original low‐frequency subbands . After that , adaptive smoothing is used to extract detailed facial information from the original low‐frequency subbands . Finally ,a facial illumination invariant is estimated by combining the detailed facial information and the blurred face image .T he resulting illumination invariant has more detailed facial information in the low‐frequency subbands ,compared with results from NSCT . The proposed method better exploits the structural information of faces .Experiments on Yale B and CM U PIE face databases show that the method effectively eliminates illumination variations ,has better performance compared with existing methods ,and improves the robustness of face recognition systems .