结合主成分分析和局部导数模式的人脸识别方法

针对基于局部模式的人脸识别方法特征维数高、计算复杂度高、识别时间长的问题,提出一种结合主成分分析和局部导数模式的人脸识别方法,并针对如何解决光照、人脸表情等方面的问题提出了改进的编码方法。该方法首先将人脸图像分成很多小的区域,然后在每一个小区域中用改进的编码方法进行编码,并建立该区域的局部导数直方图,然后采用主成分分析法对所有直方图向量进行降维得到特征向量,最后利用最近邻分类器计算相似度。实验表明,这里提出的结合主成分分析和局部导数模式方法无论在识别率还是在运算速度上都优于传统的识别算法。

Face recognition method combining principal component analysis with local derivative pattern

In order to solve the problems of high feature dimension, high computational complexity and long recognition time caused by the face recognition method based on local pattern, a face recognition method combining principal component analysis and local derivative model is presented in this paper. The improved coding method is introduced to solve the problems of illumination, facial expression and so on. The face image is divided into many small regions, and then each small area is coded by the improved coding method and a LDP histogram of the region is created. The dimension of histogram vectors are reduced by using principal component analysis. Finally, the nearest neighbor classifier is used to calculate the similarity. Experimental re- suits show that the presented method is superior to the traditional method in the recognition rate and the speed.