人脸识别技术是模式识别与人工智能的研究热点之一在生.doc

五邑大学本科毕业设计 PAGE 34 五邑大学本科毕业设计(论文) PAGE 摘 要 人脸识别技术是模式识别与人工智能的研究热点之一。在生物特征识别中，人脸识别占有极为重要的地位。它在访问控制、司法应用、电子商务和视频监控等领域都有广泛的应用。人脸特征提取是人脸识别过程的核心，特征提取的有效性直接影响到分类的速度和识别的性能。 本文的主要研究内容包括以下方面： (1) 基于Gabor变换的人脸特征提取算法。通过对人脸傅里叶变换和Gabor变换的实验比较，证明了Gabor变换在提取人脸特征方面具有很大的优越性。接着，介绍了Gabor小波变换的快速算法。 (2) 从基于图像整体代数特征PCA算法着手，主要介绍了“特征脸”算法的原理和实现过程，对组成特征投影空间的特征值选择问题，距离度量方法问题及训练样本的选择等进行了一定的研究。 (3) 研究了基于Gabor小波变换的人脸识别算法。本文研究的算法首先对人脸进行Gabor小波变换，然后应用PCA方法降低Gabor特征向量的维数，来实现对人脸的识别。实验表明，采用该方法得到的识别率要远优于单纯采用PCA方法的结果。 关键词 人脸识别；Gabor小波变换；PCA 五邑大学本科毕业设计 Abstract Face Recognition Technology(FRT)is emerging as an active research area in the field of pattern recognition and artificial intelligence.As a biometric technology,FRT has numerous applications such as access control,law enforcement,e-commerce,video surveillance and so on. Face feature extraction is the core of recognition task,which directly impact on classification velocity and face recognition ability. The main contributions of this work are listed as follows： (1) Face feature extraction algorithm based on Gabor transform is introduced.Compared with Fourier transform，Gabor transform is proved to be better in face feature extraction.And then，A fast algorithm of Gabor Transform is introduced. (2) Based on algebraic features of the images，this paper first introduced the PCA-Based face recognition algorithm.Some research has done on the selection of the eigenvector which used to create the eigen-space，the distance measure methods and the selection of the training set. (3) A face recognition algorithm base on Gabor wavelet transform is studied The algorithm studied in this paper consists of three steps：Gabor wavelet is first applied to face images，and then reduce the dimensionality of Gabor eigenvector via PCA. The improved performance of this new algorithm over pure PCA algorithm is demonstrated by the experiments. Key words face recognition Gabor wavelet transform PCA 目 录 TOC \o 1-4 \h \z \u HYPERLINK \l _Toc199305764 摘要 PAGEREF _Toc1