近年来，随着计算机技术的迅猛发展以及人们对信息安全的愈加重视，原始的身份认证方式，已经不能满足现实社会的需要，因此生物特征识别技术应运而生。与指纹和虹膜等传统生物特征识别方式相比，人脸识别技术具有准确、隐蔽和非侵扰等特性，较易被用户所接受，已在诸多领域得到了广泛的应用，并成为生物特征识别技术方面的研究热点之一。 本文在总结人脸识别技术内容和方法的基础上，对主成分分析(PCA)算法和Gabor小波变换算法这两种特征提取方法进行了重点讨论，并详细阐述了这两种特征提取方法的主要思想、特点、算法流程及实现方法。在算法实现方面，文中先对原Adaboost算法进行改进，并将其应用于人脸检测当中，而后利用PCA在提取人脸全局特征和Gabor小波在表达人脸局部特征上的优势，分别用PCA方法提取人脸全局特征， Gabor小波分块提取人脸局部特征，并将两种特征相结合建立了双层分类器(全局分类器和整体分类器)，最后，将本文提出的基于PCA和二维Gabor小波变换的人脸识别方法在ORL人脸库和自建人脸库上进行仿真实验。实验结果表明，改进算法在不影响识别速度的基础上，提高了人脸的识别率，识别率可达到90%以上，具有一定的实用价值。

In recent years, with the rapid development of computer technology and the people paying more attention on information security, the original identity authentication has been unable to meet the needs of social reality, so biometric identification technology is coming. Compared with fingerprint recognition and other traditional means of identification, face recognition technology is easier to be accepted by users because of its accuracy, concealment and non-intrusive features. Just for this reason, face recognition technology has a wide range of applications in many fields. In recent years, facial feature extraction in face recognition technology has become one of hot spot based on biological characteristics. Based on summary of the content and method of face recognition technology, this paper discusses in detail two types of feature extraction method based on subspace analysis: PCA (principal component analysis) method, and 2-D Gabor wavelet transform . Then we elaborate the main idea of the two methods in detail and introduce their algorithm process and implementation. In this paper, firstly the original Adaboost algorithm has been improved and applied to face detection. PCA has a big advantage on expression of global features of face and Gabor wavelet has a big advantage on expression of local features of face. By making use of the advantages, we extract global features of face by PCA and extract local features of face by 2-D Gabor wavelet, then we establish a double-classifier (global classifier and unified classifier) by combining the two features together. Finally, the research of face recognition technology based on PCA and 2D-Gabor wavelet transform is simulated on the ORL face database and self-built face database. Experimental results show that the improved algorithm can not only effectively improve the human face recognition rate and recognition speed, but also reduce the false recognition rate, and it still maintains high recognition rate, more than 90%, in short, the algorithm has a good practical value.