基于自适应网格矢量编码的人脸快速匹配算法

在分析传统图像匹配算法优缺点的基础上,提出了一种基于自适应网格矢量编码的人脸快速匹配算法.该算法首先对人脸进行二值化边缘分割和脸部特征轮廓提取,并对图像的细节边缘和特征轮廓进行自适应网格划分,然后对网格内的边缘段和特征轮廓段进行链码描述及矢量编码,最后通过计算编码后的方向矢量矩阵相似度来实现人脸图像的匹配识别.通过对ORL标准人脸图像库的实验仿真及对比结果表明,该算法在降低计算复杂度及提高识别精度的同时,有效地解决了由图像旋转、姿态变化引起的微小形变以及不同光照条件所带来的图像识别率低的问题.

An Approach for Fast Face Image Matching Based on Self-adaption Grid Division and Its Vector Coding

Face recognition is a new research topic in today's pattern recognition field.With analyzing the advantages and disadvantages of transient image matching algorithm,a new algorithm for fast human face image matching based on self-adaption grid division and vector coding is presented in this paper.On the premise of the edge extraction and two-value characterization,and face contour extraction,the two images' edges are firstly divided into several grids, and then the segments of the edges and character contour within each grid are described with chain and vector coding.Finally,by computing the similarities and shape alteration between the vector codes,the course of goal matching and recognition is accomplished.The simulative experiment is carried out on the standard ORL face database.Satisfied results have been obtained,which show that the proposed algorithm can not only effectively resolve the problems caused by lower recognition owing to image rotation,minute alteration of edge shape caused by pose transformation and different illumination condition,but also greatly enhance the matching precision and reduce the redundant information and the complexity of computation.