【作者】 徐吟琼；

【导师】 王子欧；

【作者基本信息】 苏州大学 ， 电子与通信工程， 2010， 硕士

【摘要】 人脸识别是计算机视觉领域里的一个非常热门的研究课题,具有极高的科研价值和广阔的应用前景。而特征提取又是人脸识别中最关键的环节之一,直接影响到识别的准确率。本文首先介绍了主成分分析、Fisher线性鉴别分析和最大散度差鉴别分析三种基于代数特征的人脸图像特征提取方法,并分析了三种方法的特点和弊端。针对传统的线性分析方法中都需要求解平均样本的共性,本文提出了两种改进方法。经过在标准人脸图像库上的实验,证实了这两种方法都较好地去除了干扰样本对平均样本的影响,能有效地提高识别的准确率。本文还尝试利用典型相关分析方法对改进后的Fisher线性鉴别分析方法和最大散度差鉴别分析方法进行特征融合,并用实验验证了所尝试的方法具有较高的识别率,达到了融合的效果。更多还原

【Abstract】 Face recognition is a top research subject in the computer visual field. It has a very high scientific value and broad application prospects, and feature extraction is one of the most critical aspects in the area of face recognition, it affects the accuracy of identification directly.This article describes three feature extraction methods for face image based on algebraic feature. They are principal component analysis、Fisher linear discriminant analysis、maximum scatter difference discriminant analysis. This article also analysis the features and disadvantages of the three methods.According to the common needs of the average sample solution in traditional linear analysis methods, this article proposes two improved methods. After a face database in the standard experiments, it proves that both the two way can remove the influence of Average samples to Interference samples, also can improve the recognition accuracy.This article also tries to use the canonical correlation analysis to fuse the features of the improved Fisher linear discriminant analysis and maximum scatter difference discriminant analysis, and through the experiments, it proved that the way we have tried has the high recognition rate, achieved the fusion results.更多还原