文档介绍：摘 要
生物认证技术是依靠人类本身所固有生理或行为特征进行身份验证一个手段。 而生物认证中人脸特征又是人和人之间相互辨识最关键和最直观生物特征。 因为人脸识别无侵害性和对用户最自然、 最直观方法, 使人脸识别成为最轻易被接收生物特征识别方法。 但因为人脸在成像过程中受到诸如光照、 表情、 姿态等多种原因影响, 使得人脸识别是目前最富挑战性研究课题之一。
本文对人体生物认证在现实生活中关键作用、 人脸检测和人脸识别技术研究意义、 系统要完成任务、 系统评价标准、 人脸检测和人脸识别技术发展关键特点、 实现方法等进行了介绍, 并指出了目前人脸检测和人脸识别领域研究关键和难点,着重介绍和推导了人脸检测和识别相关算法, 比如: AdaBoost算法、 含有局部互联结构 BP神经网络反向传输算法数学, 和在分析了传统弹性图匹配基础上提出了人脸识别基于局部特征分析(LFA)和最优化匹配人脸识别算法, 该算法首先利用神经网络方法估量出在识她人脸中起关键作用部分特征点(如瞳孔、 眼角、 眉心、 眉角、 嘴角等), 以后利用 Gabor小波局部多尺度分析特征提取特征点多尺度特征。 这么人脸每一个特征点就被一系列 Gabor小波系数所表示, 最终对待识人脸和人脸库中人脸对应特征点多尺度特征进行最优化匹配找出需要人脸。
最终, 介绍了CMOS图像传感器、 ARM9微处理器, 设计了一个基于ARM9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