摘要
传统基于主成分分析(PCA)的人脸识别算法不能最优区分不同种类样本。为此,提出一种新的基于PCA的人脸识别算法。利用PCA降维方法提取人脸的个体差异特征,并采用最近邻距离分类器对该特征进行分类。在ORL人脸数据库上的实验结果表明,与传统算法相比,该算法的正确识别率较高。
The Principal Component Analysis（PCA） is not the best method to extract features for recognition because the difference between different kinds is not considered.Aiming at this problem,a new face recognition algorithm based on PCA is proposed.It uses PCA reducing dimensions method to extract the individuality difference.A nearest neighbor classifier is employed to classify the extracted features.The method in the paper is evaluated on the ORL face image database,a series of experiments to compare the proposed approach with traditional PCA method.Experimental results demonstrate the efficacy of the algorithm.
出处
《计算机工程》
CAS
CSCD
2012年第1期146-147,共2页
Computer Engineering
关键词
人脸识别
特征提取
个体差异
主成分分析
最近邻分类
face recognition
feature extraction
individuality difference
Principal Component Analysis（PCA）
nearest neighbor classification
