文档介绍：摘要
人脸识别由于在身份认证、视觉监控以及人机接口等方面有着广泛的应用前景,从而成为目前模式识别和计算机视觉领域的一大研究热点。人脸识别涵盖了图像处理、模式识别、神经网络、计算机视觉、生理学以及数学等诸多学科,是一项非常综合的技术,它的应用正随着社会的进步与日俱增。人脸识别是生物测定学研究的内容之一, 是模式识别领域中的一个前沿课题。目前, 人脸识别逐渐成为模式识别和人工智能领域的一个研究热点。但由于复杂的光照条件下, 多变的人脸表情以及姿态的变化都增加了人脸自动识别的难度, 尽管人脸识别已经取得了较大的发展, 但离实际应用仍有较大差距。作为一种经典的模式识别问题, 计算机人脸识别的成功离不开合理的特征提取和有效的分类器设计策略。
在人脸识别及其他模式识别领域中,特征提取是一个非常有意义的研究方向。到目前为止,有很多相应的算法应用到人脸识别领域,其中比较著名是基于Fisher线性鉴别准则的Fisherface方法、LDA算法PCA算法。本文基于MATLAB的人脸识别环境,设计并实现了一个基于Fisherfaces的人脸识别算法实现系统,展示如何通过利用MATLAB 的工具函数和多种算法实现对人脸识别的各种处理。论述了利用设计的系统实现人脸识别进行打开、操作、保存、另存、打印、退出等功能操作。
关键字
人脸识别;Fisherface;MATLAB
Abstract
 Face recognition has a wide range of applications due in authentication, visual surveillance , and human-machine interface, thus e a major research focus of pattern recognition puter vision . Face covers image processing, pattern recognition, works, computer vision, physiology and mathematics , and many other disciplines, is a prehensive technology, its applications are increasing with the progress of society . Face recognition is one of the elements of biometrics research in the field of pattern recognition is a leading subject . Currently , face recognition is ing a hot topic in the field of pattern recognition and artificial intelligence . However, due to plex lighting conditions , facial expressions and posture change changing face have increased the difficulty of automatic face recognition , face recognition despite great progress has been made , but there is still a large gap from practical application . As a classic pattern recognition problems , inseparable from the ess of a reasonable feature extraction and face recognition classifier design effective strategies .
In face recognition and other pattern recognition , the feature extraction is a very interesting research direction . So far , there are many appropriate algorithm is applied to face recognition , which is based on the more famous Fisher linear discriminant criterion Fisherface method , LDA algorithm PCA algo