特征子空间法人脸识别研究

【作者】 孙鑫；

【导师】 刘本永；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2005， 硕士

【摘要】 人脸识别因其在安全验证系统、信用卡验证、医学、档案管理、视频会议、人机交互、系统公安(罪犯识别等)等方面的巨大应用前景而越来越成为当前模式识别和人工智能领域的一个研究热点。虽然人类能毫不费力地识别出人脸及其表情,但人脸的自动机器识别却是一个难度很大的课题。它涉及到模式识别、图像处理及生理、心理学等方面的诸多知识。本文以模式识别的特征子空间思想为主要研究方法,以人脸识别的关键步骤为主要内容,系统地研究了人脸识别技术。在预处理环节中,将基于像素平均的降维处理、基于能量的光照归一和基于Fourier 变换的脸像平移效应消除三种简单易行的算法相结合;在特征提取环节,为避免“小样本问题”,采用基于主元分析(PCA)的特征子空间方法提取本征脸(Eigenface)或本征谱(Eigenspectra),提出了局部本征谱(Local Eigenspectra)的脸像特征提取算法,以突出人脸的局部特征;在分类器设计环节,重点讨论了非线性Parzen 分类器中核函数的参数估计算法。对ORL 脸像数据库的实验结果表明:在预处理中增加能量归一化和Fourier变换提取幅度谱,可以消除脸像的平移影响,明显地提高正确识别率;基于局部本征谱的特征提取手段相较传统的特征子空间提取手段有较高的识别率;相较常用的欧式距离分类准则而言,将Parzen 分类器应用于人脸识别有助于提高识别性能。更多还原

【Abstract】 Face recognition is a complex and difficult problem which is important for surveillance and security, telecommunication, digital libraries, video meeting, and human-computer intelligent interaction. Despite the fact that human faces are essentially similar, we are very skilled at recognizing the identities of people from their faces. We can perform this task very easily and it is a basic and important social act although we are still puzzled with the psychological and physiological nature of the process. This thesis studies the theories and methods of FR (face recognition) systematically, focusing on subspace pattern recognition. In the preprocessing stage, we propose to combine pixel averaging, energy-normalization, and the Fourier transform to obtain a vector which has relatively low dimension, low sensitivity to brightness variation and to face shifting in the image plane. In the following feature extraction, we adopt the PCA or K-L transform-based subspace method to avoid the small sample size problem, a general problem in a face recognition system. The extracted eigen features are called eigenspectra or eigenface, depending on whether the Fourier transform is performed or not at the preprocessing stage. Basing on eigenspectra, a local eigenspectra-based scheme for face recognition is addressed in this stage. The classification stage focuses on estimating the parameter of the Gauss kernel for the nonlinear Parzen classifier. Experimental results on the Olivetti Research Laboratory (ORL) face database show that performing energy-normalization and the Fourier transform in preprocessing stage is feasible; better than the traditional eigenspace method, the local eigenspectra-based scheme gets higher recognition rate; comparing with the Euclidean distance rule, the Parzen classifier is more powerful for face recognition.更多还原