基于局部特征分析与最优化匹配的人脸识别算法

Face Recognition Algorithm Based on Local Features Analysis and Optimization Matching

在分析传统弹性图匹配的基础上,提出一种基于局部特征分析(LFA)与最优化匹配的人脸识别算法.该算法首先利用神经网络方法估计出在识别人脸中起重要作用的一些特征点(如瞳孔、眼角、眉心、眉角、嘴角等),之后利用Gabor小波的局部多尺度分析特性提取特征点的多尺度特征.这样人脸的每个特征点就被一系列的Gabor小波系数所表示,最后对待识人脸与人脸库中人脸的相应特征点的多尺度特征进行最优化匹配找出需要的人脸.对最优化匹配方法给出了严格的数学证明,同时也给出了Yale大学和ORL人脸库上的测试结果.理论和实验证明,该方法远优于传统的EigenFace方法,同时能有效地克服光照变化对人脸识别的影响,在一定程度上对表情的变化也有较好的鲁棒性.

On the basis of the analysis of traditional elastic graph matching, a face recognition algorithm based on local feature analysis and optimization matching is proposed. Firstly, some important face features are located by means of neutral network. Secondly, the multiscale features of the feature points are extracted with the local mutiscale analysis feature of the Gabor wavelet. In this way, every face feature point is represented by a series of Gabor wavelet coefficients. Finally, in order to find the face needed, the test face is compared with the multiscale features of the corresponding feature points in the face database with the optimization matching. Here the optimization matching method is proved strictly. The test results about Yale and ORL face database show that not only the proposed method is far better than the traditional EigenFace method but also the effect of the illumination variation on the face recognition is obviously overcome and the method has quite good robust for face expression variation in some degree.