当今社会，随着计算机与科学技术的迅速发展，信息领域的安全愈加受到大家的重视，而传统身份识别方式的不准确性、可替代性，明显已经大大不能满足当代社会的需求，因此依据生物自身特征来识别身份的各项技术不断涌现。如手掌指纹、人眼的视网膜和虹膜以及行为习惯（笔迹、形态）等，其中人脸识别技术不同于其他生物特征识别技术，具有识别简单方便、特征确定唯一的特点，可以分别从主动和被动两种方式完成识别工作，从而得到了学者们深入的研究以及市场的广泛应用，不言而喻已经成为信息领域颇受关注的生物特征识别技术。 本文通过对现有人脸识别技术的研究与分析对比，结合各自的优缺点，确定了使用二维主成分分析(2DPCA)和Gabor小波变换这两种算法，对其特征提取进行了深入的研究，并对这两种算法的主要研究思想、各自的特点以及特征提取的流程、实现情况作了详细的阐述。然后本文对人脸检测算法中的Adaboost算法进行了简单改进，提高了检测的精确度和准确率，并且在特征提取和分类器设计上做了改进，先通过2DPCA提取全局特征来构建一个全局分类器，然后用二维Gabor小波变换提取局部特征，根据空间位置构建多个局部分类器，最后以加权求和的方式进行并行集成，构成最终的整体分类器。最后将该算法在国际通用ORL人脸库和自建的人脸库中进行了实验仿真。实验结果表明，本文提出的两种算法相结合的人脸识别算法，在不影响人脸识别速度的前提上，其识别率可以达到97%左右，达到了预期的目的。

Nowadays, with the rapid development of computer technology and science ,people even pay more attentions aboat the information in the field of security . But the inaccuracy and irreplaceable of the traditional identification methods obviously can not greatly meet the demand of contemporary society . Therefore, the technology based on their biometric to identify the identity is emerging. Such as palm fingerprint, iris and retina of the eye as well as the human diet (handwriting, shape) etc. Face recognition technology which is different from other biometric technologies has the characteristics aboat easy identification and the only characteristic determining. You can complete the identification work separately from the active-passive in two ways.So it gets the in-depth research of scholars and a wide range of applications on markets,and self-evident has become the field of information flurry of biometric technology Through research and analysis comparison of the existing face recognition technology, combined with their advantages and disadvantages, I determine using two-dimensional principal component analysis (2DPCA) and Gabor wavelet transform of these two algorithms,and make the in-depth research on extracting its features.Then we make a detail on the main idea and their characteristics as well as the processes and the achievement on feature extraction aboat the two algorithms. Then this paper make a simple improvements on face detection algorithm Adaboost algorithm, it improves the accuracy and detection accuracy, and have been made improvements on feature extraction and classifier design.Firstly, let's build a global classifier by 2DPCA global feature extraction, then using two-dimensional Gabor wavelet transform extracte the local features and building multiple local classifier based on spatial location. Finally, it constitutes the final overall classification as the weighted sum of the way parallel integration，and conducts simulation experiments on International General ORL database and self Face library. Experimental results show that, this paper proposes two methods of face recognition algorithm combining can reach 97% on the premise of no influence on the speed of face recognition,and it achieves the desired goal.