摘要
对人脸识别的几个关键技术进行了深入研究 ,提出了一种快速的基于眼睛像素特征的人脸检测方法 :一种有效的基于SVD分解的特征提取方法和一种基于特征差别的SVM人脸识别方法 改进的基于SVD分解的特征提取方法能在一定程度上削弱光照和表情的影响 ,从而更好地抽取人脸的差别特征 基于特征差别的SVM方法将人脸识别这一典型的多分类问题构造成适合SVM处理的二分类问题 ,克服了传统SVM方法在解决多分类问题上的缺陷 实验表明该人脸检测方法有较高的正确检测率 ,提出的特征提取方法能有效地减弱光照和表情对人脸特征的负面影响 ,使得识别率有较大提高 。
Some key problems in face recognition are studied in this paper. A new method of face detection,a new SVD-based method of feature extraction and a new method of face recognition based on SVM are proposed. The improved method of feature extraction based on SVD can extract face features better than the traditional PCA. Only one SVM is built to solve face recognition,which is a typical problem of multi-classification,thus overcoming some flaws of several traditional multi-classification methods of SVM. The experiments are done on the FERET,BioID and the face databases with manual as well as automatic face detection means. The results show that this new detection method presents high correct rate. The new feature extraction method based on SVD weakens the influence of illumination and expression on recognition in order that the higher rate of correct recognition is obtained. The new face recognition method based on SVM has better capability of generalization and higher rate of correct recognition than other SVM methods. These new methods provide academic and experimental warrant for building an automatic face detection and recognition system based on SVM.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2004年第7期1074-1080,共7页
Journal of Computer Research and Development
基金
国家自然科学基金项目 ( 60 2 73 0 3 3 )
江苏省"十五"科技攻关项目 (BE 2 0 0 10 2 8)
关键词
人脸检测
特征提取
支持向量机
人脸识别
face detection
feature extraction
support vector machines
face recognition
