【作者】 汪晓妍；

【导师】 傅德胜；

【作者基本信息】 南京信息工程大学 ， 系统分析与集成， 2006， 硕士

【摘要】 自动人脸识别(AFR)研究试图赋予计算机根据人脸辨别人物身份的能力。该研究具有重要的科学意义和巨大的应用价值。经过三十多年的发展,AFR技术取得了长足的进步,目前最好的AFR系统在理想情况下已经能够取得可以接受的识别性能。但测试和实践经验表明:非理想条件下的人脸识别技术还远未成熟,要开发出真正鲁棒、实用的AFR应用系统还需要解决大量的关键问题,尤其需要研究作为识别必要前提条件的面部关键特征精确定位问题。本文重点探讨基于统计学习的面部特征定位问题。 研究了特征精确配准问题,重点讨论了基于主动形状模型的人脸定位算法。首先介绍了点分布模型,并在训练样本对齐、形状变换建模和基数目的选择三方面展开讨论,然后详细描述了主动形状模型的整个搜索过程和多分辨率框架,并讨论了相关研究工作。从统计外观模型出发,介绍了基于统计外观模型的AAM技术,并与ASM做了比较说明。同时介绍了图像扭曲技术,重点描述了分块仿射技术。 介绍了人脸定位样本的采集过程,搜集各种人脸图片进行人工标定关键特征点,利用主动形状模型(ASM)进行人脸定位。提出模型分次搜索,在对整个人脸建模的同时,也分别对眼睛和嘴巴构造模型,利用眼睛模型和嘴巴模型优化总体搜索达到了非常好的效果。通过模型点数对比实验和去除轮廓实验来对模型描述选择进行讨论,计算了平均搜索误差和搜索一次所耗时间,并进行了对比分析。对测试中失败结果进行分析,对影响搜索的各种因素进行讨论,指出初始化对于搜索的成败往往起了决定作用,而光照变化、姿态变化、表情变化、毛发饰物的遮挡、训练集不足等因素的影响也是整个搜索失败的重要原因。 指出基于ASM的人脸定位缺少收敛准则和质量评价,强调了建立一种合理的质量评价机制的重要性,提出了一种利用统计学习方法来构造人脸定位评估函数的方法。介绍和讨论分类器设计,指出分类器性能主要取决于特征空间和学习算法选取两个方面。观察和分析了Gabor小波,指出其优良特性(良好的空间局部性和方向选择性)并选择Gabor特征作为评估算法的分类特征。介绍和研究了AdaBoost学习算法,选用AdaBoost学习算法来设计用于定位评估的分类器。实验结果证明此分类方法效果良好,并在语义上更有意义。更多还原

【Abstract】 Automatic Face Recognition (AFR) aims at endowing computers with the ability to identify different human beings according to face images. Such a research has both significant theoretic values and wide potential applications. After more than 30 years’ development, AFR has made great progress especially in the past ten years. The state-of-the-art AFR system can perform identification successfully under well-controlled environment. However, evaluation results and practical experience have shown that AFR technologies are currently far from mature. A great number of challenges are to be solved before one can implement a robust practical AFR application, especially the accurate facial feature location problem, which is the prerequisite for sequent feature exaction and classification. In this thesis, facial point location using statistical learning methods is studied after a recent overview of AFR research and development.Study facial feature alignment problem and provid a thorough survey of the algorithms, and then focus on face alignment using Active Shape Model. Point Distribution Model is described, and three aspects of ASM are discussed: aligning the training set, modeling shape variation and choice of number of modes. Active Appearance Model and Image Warping technique, especially the Piece-Wise affines algrithom, are described.Process of training examples collection is introduced, and key points are marked in images and models are trained using ASM for face alignment. Three models are builded for the search, including model of eyes, model of mouth, and model for the whole face. When searching a new face, we use the whole face model first, and then use the other two to adjust the result. Our experiments have illustrated the better performance on face alignment. Representation of model is discussed with the experiment using different numbers of key points and the experiment using amodel without the face outline. Average search time and point-to-point error are computed and compared. Reasons for the fail results are investigated, like changes of illumination, pose, expression and so on.Research on alignment evaluation problem, and propose a statistical learning approach for constructing an evaluation function, as the lack of convergence guide line and evaluation of ASM alignment results. The design of classifier is discussed, and the performace of classifier is decided by the selection of feature space and learning algorithm. Gabor wavelets are observed and AdaBoost learning method is introduced. Then a nonlinear classification function using Gabor feature and AdaBoost learning method is learned from a set of positive (good alignment) and negative (bad alignment) training examples to effectively distinguish between qualified and un-qualified alignment results. Experimental results demonstrate that the classification function learned using the proposed approach is effective and provides semantically more meaningful scoring.更多还原