【作者】 夏小东；

【导师】 张家树；

【作者基本信息】 西南交通大学 ， 信号与信息处理， 2007， 硕士

【摘要】 当前人脸识别技术已经成为计算机视觉和模式识别领域的研究热点，它具有广泛的应用领域和重要的理论研究价值。人脸识别技术主要包括人脸检测和人脸识别两个方面。人脸检测要走向实际应用，检测精度和速度是两个关键问题；多角度人脸检测是人脸检测中的一个难题。由于人脸的三维非刚体特性，同一人脸在不同姿态、光照条件下表现出很大的差异性，人脸识别算法的鲁棒性和速度都需要提高。本文使用基于统计的模式识别方法针对这些问题进行研究，主要工作内容有以下三点：1、分析了现有人脸检测方法的优缺点，提出了一种基于Census特征和CS-AdaBoost的实时人脸检测方法。该方法使用了一种更加有效的统计学习算法来训练样本，并且采用了一种多分辨率图像金字塔的搜索策略。实验证明，本方法提高了检测率，降低了误检率，而且新的搜索策略加快了检测速度。另外，本文还将改进后的方法应用于多角度人脸检测。2、对HMM在人脸识别中的应用进行了研究，分析了不同特征抽取方法对识别效果的影响，提出了一种基于局部奇异值和HMM的人脸识别方法。本方法采用局部奇异值向量作为HMM的观察序列，能够更加充分地利用图像的细节信息，用优化的模型进行人脸识别。对比其它基于HMM的人脸识别方法，本方法识别率更高，在ORL数据库上最高获得了99％的识别率，而且识别时间更短。3、对人脸识别中的光照问题进行了研究，分析了相位信息可以消除光照影响的特性，提出了一种基于相位重构和DiaPCA的人脸识别方法，并与几种经典的统计识别方法进行了对比。实验证明，在光照人脸识别问题上，本方法的识别率有了很大的提升，尤其当特征矩阵维数很小时，计算量大大降低，而且识别率也高于其它方法，可以用于快速光照人脸识别。更多还原

【Abstract】 Face recognition is one of the most challenging problems in the fields of pattern recognition and machine vision. It also becomes an active research topic recently. A robust face recognition system should contain detection and recognition. The detect rate and speed are two key problem of face detection, and multi-view face detection technique is still a challenging problem. Although face recognition technique develops quickly along with various applications, there are still many problems unsettled yet, for example, the robustness of recognition method and the influence of illumination.This thesis focuses on the problems presented above using statistical method and the main researches are as the follows.First, a new method about face detection using Census feature and Cost-Sensitive AdaBoost (CS-AdaBoost) is proposed. Census feature can present all the local spatial structures of an image, and it is robust to illumination. CS-AdaBoost is a new version of AdaBoost method, it can achieve higher detect rate during less iteration. Results show that this method can achieve higher detect rate than Froba’s method, and it is faster than Viola’s method because a multi-resolution image pyramid is used. Three view based face detectors are trained to solve the multi-view face detection.Second, a method that based on local singular value decomposition and HMM is presented. Local singular value decomposition is used to subtract the character of the face image. As a result, the recognition rate is increased and the computation is less then Liu’s method.Finally, a method that based on phase-only reconstruction image and DiaPCA is presented. In the Fourier representing of signals, magnitude and phase tend to play different roles and in some situations phase contains more illumination information while magnitude contains texture information of an image. It is proved that DiaPCA is more accurate than original PCA. Phase spectrum combined with DiaPCA without any pre-processing is used to overcome the problem of illumination. Extensive experiment shows that the proposed method can achieve very encouraging performance in varying illumination conditions.更多还原