基于形状和纹理的人脸自动识别

人脸识别技术在商业和法律上有广泛的应用前景，在安全监控中也大有用武之地。其主要任务是利用已有的人脸图像库，识别静止的或视频图像中的一张或多张人脸．实验首先提取人脸图像的形状和纹理特征，运用广义KL变换降低形状和纹理空间的维数，避开入脸识别小样本集的局限,同时，通过运用具有统计不相关性的最佳鉴别变换，来抽取人脸图像的有效鉴别特征．在包含960幅人脸图像的NUST603人脸图像库上进行识别实验，得到的识别错误率低于4％，且这种方法对人脸的姿态、表情等条件有一定的不敏感性．

A Shape-and Texture-Based Face Recognition

Face recognition technology (FRT) has numerous commercial and law enforcement applications, especially in video surveillance The primary task at hand, given still or video images, requires the identification of one or more persons using a database of stored face images A new face coding and recognition method is first introduced based on integrated shape and texture features And the dimensionalities of the shape and texture spaces are reduced using generalized KL transform The corresponding reduced shape and texture features are then processed by the uncorrelated discriminant transform Experiments on the database NUST603 of 960 face images indicates an error rate of 4% Experimental results also show that this method is not sensitive to the pose and expression of human faces