近年来，由于人脸识别具有广泛的应用前景，其逐渐成为计算机视觉与模式识别领域的研究热点，人脸分析中包含的课题很多，如：人脸检测、人脸跟踪、人脸识别、表情分析等。本文以人脸识别及相应算法分析作为研究重点，提出了两种研究人脸识别的新思路： 1、基于“特征眼”小特征量的人脸识别算法。针对人脸眼睛的局部特征，本文设计了一种能够匹配人眼的haar特征模板，通过用本文模板对yale人脸图像库中样本图片进行人眼提取试验，证明了本文模板是有效的；然后通过二次线性插值算法对人眼区域进行统一化；将统一化的人眼图像数据各列首尾相接组成一个特征，通过主成分分析法提取人眼区域特征组成特征眼；将待测人眼映射到特征眼空间中并使用改进的级联Adaboost算法进行识别。经过试验分析证明本文算法能够在20个特征量的条件下达到90%左右的识别率，相当于传统主成分分析法中需要60个特征量才能达到的识别率，能够大大的降低算法时间复杂度，具有广泛的应用前景。 2、基于三维图像的人脸识别算法。本文结合当下视频技术热点和三维图像的特点，本文提出了基于三维图像的人脸识别技术。由于三维图像不能直接转换为灰度进行计算，本文使用HSL色彩空间中的HS分量进行特征提取，既降低了计算维度，又避免了将图像转换为灰度图像。然后结合PCA方法计算待测图片的HS分量到样本的距离，通过计算两个分量距离的加权和，找出到样本的最小距离达到识别的目的。本文经过实验得出当权值取0.75时，采用本文的算法识别率达到95%以上。相比基于灰度的人脸识别方法本文提出的基于三维图像的识别算法具有更高的识别率，并且所需的训练样本也较少。随着三维图像采集设备的普及，本算法具有一定的理论意义及实用价值。

Recent years, because of widely used, face analysis has become a hot research field of computer vision and pattern recognition. face analysis contains many topics, such as face detection, face tracking, face recognition, expression analysis and so on. This thesis is focused on face recognition. We have developed two new method for face recognition. 1.Face recognition algorithm featured small characteristic and based on Characteristic Eye. In this thesis we designed a HAAR template which can match human eye in the basis of individual features of human eyes, and the effectiveness of the template has been proved after a human eye extracting test was carried out by using sample pictures of Yale human face image library. We firstly unified the human eye area through the bilinear interpolation algorithm. Then we connected the unified human eye image data end to end to form a feature and then extracted those features through the principal component analysis (PCA) to obtain characteristic eye. Ultimately we present the target human eye images to the characteristic eye space to achieve face recognition aim. After experiments and analysis it has been proved that in a given situation of 20 characteristic quantities this algorithm is able to reach a recognition rate of around 90%, which is equivalent to recognition rate that the traditional PCA can attain with 60 characteristic quantities. That means this algorithm for face recognition can greatly reduce the time and complexity the traditional algorithm needs and may be widely used. 2. A new face recognition algorithm based on 3D images. Combined with the current popular video technology and the characteristics of 3D images, we present this algorithm for 3D face recognition in this thesis. Since 3D images can not be converted into grayscale images directly , we adopted image in HSL color space to extract image features which reduced the computational dimension. Combining the PCA method we calculated the distance between the HS space of our target images and the samples, and then we calculated the distance between two components to get their weighted sum, and eventually by finding out the minimum distance to the samples to reach face recognition aim. After experiments we found out that when give the weight 0.75 we could reach a face recognition rate up to 95%. Compared with the face recognition method based on grayscale images, this 3D face recognition algorithm provided a higher recognition rate and required less training samples. With the popularity of 3D image acquisition equipment, this image collection the popularity of equipment, this algorithm for 3D face recognition also has the significance for popularization.