【作者】 马俊伟；

【导师】 王国营；

【作者基本信息】 解放军信息工程大学 ， 计算机应用技术， 2008， 硕士

【摘要】 基于生物特征的身份认证技术在社会生活中具有越来越重要的地位和作用,已逐渐成为信息安全的重要研究领域。在多种生物特征中,基于人面部特征的识别和认证因为具有无侵害性、成本低、隐蔽性、不需要被测者特殊配合等优点,得到了广泛的关注和重视,具有广阔的应用前景。本文通过对几种常见的身份认证技术的分析,总结了现有的身份认证系统存在的问题和不足,在此基础上将生物特征之一的人脸特征应用于身份认证技术中,设计并实现了一个基于人脸特征的身份认证原型系统,对系统的功能、框架结构和涉及的关键技术等进行了研究和分析,并对所设计系统的性能进行了测试。结果表明,该系统能够较为准确地完成身份认证任务,且对一定范围内的人脸姿态、光照变化等具有较强的健壮性。研究的关键技术包括人脸和眼睛的检测与定位、图像预处理、人脸特征提取和分类器设计等。本文利用AdaBoost学习算法构建了人脸检测和眼睛检测分类器,实现了人脸和眼睛的检测与定位,实验表明,该算法能够实时、准确地检测并定位出人脸和眼睛。针对人脸图像存在的姿态、光照变化等问题,结合人眼定位时的眼睛坐标和图像预处理技术提出了人脸图像的规范化处理方法,有效地减少了姿态、光照变化对系统性能带来的影响。特征提取方法的优劣直接影响到系统的认证性能。由于Gabor小波核函数具有较强的空间位置和方向选择性,能够捕捉对应于空间和频率的局部结构信息,表达人脸最有用的局部特征,因此本文采用人脸图像的Gabor滤波响应来表示人脸特征。为解决Gabor特征维数过高的问题,结合主分量分析和线性鉴别分析方法各自的优势提出了两种特征提取方案,并在简单分类器和SVM分类器上进行了实验。实验表明,GP特征提取方案在SVM分类器上获得了较好的实验效果。更多还原

【Abstract】 The identity verification technology based on biometric features played an increasingly important role in our society; it has been a key research area in information security. Among the measured biometric features, facial feature identification and verification are gaining popularity and diverse applications for the reason that they are considered to be non-invasive, low cost, and natural biometric technologies.In this thesis, several common identity verification technologies are analyzed and the problems and shortages of the present identity verification system are summarized. Base on this, face features are applied to identity verification technology and a face-based identity verification prototype system is designed and implemented. The function module, frame, and key technologies of the system are researched and analyzed, and the performance of the system is tested. The results show that the system can achieve identity verification task precisely, and robustness to pose and lighting variance.The key technologies in research include the detection and location of face and eyes, the image pre-processing, the extraction of facial features and the design of classifier. In this thesis, AdaBoost learning algorithm is applied to construct the classifier to realize face detection and eye location. The result shows that the algorithm can detect and locate face and eyes timely and precisely. Face image pre-processing method is put forward, which reduces the affect of pose and lighting variance on the system.That whether the method of feature extraction is good or not can directly affects the verification ability of the system. Due to the better spatial and orientation selection of the Gabor wave kernel function which can capture local structure informations corresponding to spatial and frequency to express the most useful local features of faces, the thesis use the Gabor response of face image to represent face features. In order to deduce the high dimensions of the Gabor features, the basis of researching the advantages of both the principal component analysis and linear discriminant analysis, two feature extraction methods are put forward and examined on simple classifier and SVM classifier. The results show that GP feature extraction method on the SVM classifier obtains the better result.更多还原