基于复合梯度向量的人脸识别算法

A Face Recognition Algorithm Based on Composite Gradient Vector

提出了一种新的基于复合梯度向量(Composite gradient vector,CGV)描述的人脸识别算法.该算法首先在定位后的人脸图像中标定目标区域,并在目标区域内划分特征子区域,然后,以特征子区域的边缘奇异点作为向量的起点和终点进行正交采样得到基向量,将目标区域内所有基向量组建向量簇,通过对基向量的多维复合得到向量簇内所有极大梯度向量,最后,以极大梯度向量作为元素组建复合梯度向量并统计复合梯度向量的维度和梯度信息,将复合梯度向量、复合梯度向量维度和梯度进行人脸库对比,识别出人脸身份.该算法抓住了人脸面部特征分散性的特点,继而对分散性特征采用具有连续性规律约束的复合梯度向量进行描述识别.实验结果表明,该算法克服了特征域旋转、光照强度变化及多姿态、多表情对人脸识别的影响,具有速度快、识别准确、适应性强的特点.

A novel approach to face recognition based on the composite gradient vector （CGV） is proposed in this paper. Firstly,by detecting the target area information in the located facial image,several image segments are made ready for getting the base vectors with the edges of the sub-area. All the base vectors are made into vector groups. Then the great gradient vector which is extracted from the vector groups constitutes the composite gradient vector. The dimensions and gradient information are calculated for face recognition. Finally,recognition results are obtained by gradient and dimension both of which are extracted from the composite gradient vector. Experiments show that the proposed approach has overcome the feature rotating,varied pose,different facial expressions,and achieves good recognition results.