毕业论文：人脸识别系统.doc

摘 要 生物认证技术是依靠人类自身所固有的生理或行为特征进行身份验证的一种手段。而生物认证中的人脸特征又是人与人之间互相辨识的最重要和最直观的生物特征。由于人脸识别的无侵害性和对用户最自然、最直观的方式，使人脸识别成为最容易被接受的生物特征识别方式。但由于人脸在成像过程中受到诸如光照、表情、姿态等各种因素的影响，使得人脸识别是当前最富挑战性的研究课题之一。 本文对人体生物认证在现实生活中的重要作用、人脸检测和人脸识别技术研究的意义、系统要完成的任务、系统的评价标准、人脸检测和人脸识别技术的发展的主要特点、实现方法等进行了介绍，并指出了当前人脸检测与人脸识别领域的研究重点与难点,着重介绍和推导了人脸检测和识别的相关算法，比如：AdaBoost算法、具有局部互联结构的 BP神经网络反向传播算法数学，以及在分析了传统的弹性图匹配的基础上提出了人脸识别的基于局部特征分析(LFA)与最优化匹配的人脸识别算法，该算法首先利用神经网络的方法估计出在识别人脸中起重要作用的一些特征点(如瞳孔、眼角、眉心、眉角、嘴角等)，之后利用 Gabor小波的局部多尺度分析特性提取特征点的多尺度特征。这样人脸的每一个特征点就被一系列的 Gabor小波系数所表示，最后对待识人脸与人脸库中人脸的相应特征点多尺度特征进行最优化匹配找出需要的人脸。 最后，介绍了CMOS图像传感器、ARM9微处理器，设计了一个基于ARM9的人脸识别系统。 关 键 字：生物认证、人脸识别、神经网络、ARM Human face recognition system Abstract Biometric recognition realizes personal identification based on the inherent physical or behavioral characteristics of human being, among which facial features are the most important and intuitional ones. Face recognition has become the most acceptable biometric recognition method because of its no violation of privacy and intuition. Face recognition is challenging in the pattern recognition due to the influence of illumination|, expression and pose. In this paper, we present a comprehensive review of face detection and recognition, which includes the face detection, face recognition and the current development in the literature. We also give a presentation of the major points and challenges in the face detection and recognition research. On the base of our research, introduced and transmit algorithm bout face detection and face recognition, such as: AdaBoost algorithm, the proof for BP Neural network algorithm. And traditional elastic graph matching is analyzed and a face recognition algorithm based on local feature analysis and optimization matching is put forward. Firstly, some important face features (such as pupil, canthus, center of eyebrow, corners of eyebrow, corners of mouth) are located using neutral network. Secondly, the multiscale features of the feature points