融合Gabor特征的局部自适应三值微分模式的人脸识别

Face Recognition Based on Local Adaptive Ternary Derivative Pattern Coupled with Gabor Feature

提出了一种融合Gabor特征和局部三值微分模式(LTDP)的人脸识别算法,并在该算法中对LTDP方法进行改进,提出了自适应阈值算子。通过Gabor滤波器对人脸图像进行滤波,提取人脸多尺度、多方向的幅值特征;针对每个幅值特征图像,运用局部Gabor自适应三值微分模式(LGATDP2)方法提取局部方向关系模式;通过模式区域直方图统计,计算信息熵权重,加权串联后得到最终的人脸描述。识别过程中,采用χ2距离对特征直方图进行相似度匹配。分别在ORL和Yale数据库中进行仿真。结果表明,LGATDP2方法具有更好的普适性,在光照变化、表情变化和噪声干扰下具有更高的稳健性。

A face recognition algorithm based on fusing Gabor feature and local ternary derivative pattern （LTDP） is proposed. The LTDP method is improved and an adaptive threshold operator is presented in this algorithm. Face images are filtered by the Gabor filter to extract face magnitude features with multi-scale and multi-orientation. The local Gabor adaptive ternary derivative pattern （LGATDP2） method is used to extract the local orientation relationship patterns of magnitude feature images. The information entropy weight is calculated by the pattern area histogram statistics, and the face image is described after the weighted series. The similarity match of the feature histograms is carried out with the chi-square distance. The simulations are conducted in the ORL and the Yale databases. The results show that the LGATDP2 method has better universality, and it is more robust under the conditions of illumination variation, expression changing and noise interfer.