【作者】 韩丽；

【导师】 李锵；

【作者基本信息】 天津大学 ， 信号与信息处理， 2009， 硕士

【摘要】 人脸识别技术是利用计算机分析人脸图像,从中获取有效识别信息,用来辨认身份的一门技术。与其它生物识别技术相比,人脸识别具有直接、友好、方便、非侵犯性、稳定等优点,因而有着广阔的应用前景。本文针对一些已经存在的人脸识别方法进行改进,推动了人脸识别技术的进一步发展。针对人脸图像维数较大,直接进行处理时计算复杂度较大的问题,本文利用小波分解对人脸图像进行了预处理。取经过小波分解后的低频部分作为预处理后的图像,降低了图像的维数,去除了部分高频噪声,在不影响识别精度的同时降低了训练时的计算复杂度。针对主成分分析方法(PCA)只能消除图像二阶冗余的缺点,本文利用盲信号处理中的高阶数据处理方法——独立成分分析方法进行人脸图像的特征提取,不仅提取了图像的二阶信息,也充分利用了人脸图像的高阶统计特性,提取的特征能更好地揭示人脸图像的本质特征,为进一步分类打好基础。分类识别阶段,由于人脸特征具有小样本、非线性及高维模式等特点,而支持向量机(SVM)在解决这类问题时表现出许多特有的优点,所以,本文将支作持向量机为分类器应用于人脸识别,与简单的距离分类器相比,大大提高了分类效果。最后,本文基于ORL人脸库对各种算法进行了计算机仿真实验,给出了具体的实验数据及结果分析。更多还原

【Abstract】 The face recognition technology is to analyze face images with computer, obtain the valid information and realize the identification of human faces. Compares with other biometrics, the face recognition technology is direct, friendly, convenient, non-infringe and stable, therefore, the face recognition technology has an extremely broad application prospect. In this paper, some existing methods for face recognition are improved and face recognition technology is promoted.For the problem that the dimension of face images is large and it is difficult to deal with such large images directly with computer, in this paper, wavelet decomposition is used as a pre-processing method. The result of the wavelet decomposition is that the dimension of the images is reduced and some high-frequency noise is filtered. Thus, the training computational complexity is reduced without affecting the identification accuracy.Because of the shortcomings of the Principal Component Analysis (PCA) that it can only eliminate the second-order redundancy in the images, in this paper, Independent Component Analysis (ICA)-a kind of high-order data processing method in Blind Signal Processing (BSP) field, is used to extract the features of the face images. The ICA method not only extracts the second-order information between images, but also makes full use of the higher-order statistical features of the face images. The features extracted by ICA method can reveal the nature of the face images better. As a result, it makes a good foundation for further classification.In classification stage, the facial feature is characteristic of small sample, nonlinearity and high-dimensional model, and Support Vector Machine (SVM) shows a number of unique advantages in solving this kind of problems. So, SVM is applied to face recognition as a classifier in this paper. Compared with the simple distance classifiers, the classification results gotten by using SVM are significantly improved.At last, based on ORL face database, the simulation experiment is realized by computer with a variety of algorithms. The experimental data and result analysis are given in detail.更多还原