摘要
现有高阶特征的人脸识别由于对噪声敏感导致鲁棒性差以及特征冗长,为此提出一种基于“梯度脸”的局部高阶主方向模式的人脸识别方法.首先设计梯度脸卷积算子,计算像素的多方向梯度分量和,以构造梯度脸;然后在梯度脸上引入主方向分组策略表征其高阶导数特征,以局部邻域高阶导数方向变化的特征码形成主方向特征图;最后分块统计其直方图特征并级联,并利用多分类支持向量机完成分类识别.在多个公开人脸库中的实验结果表明,该方法对光照、表情和面部遮挡等变化因素具有良好的鲁棒性,以及更高的识别效率.
Pointing to weak robustness caused by the noise sensitivity and feature redundancy of present face recognition methods with high-order features, a new method of the local high-order principal direction pattern based on "gradient face" is proposed. Firstly, the gradient face convolution operator designed is used to compute the sum of multi-directional gradient components of pixels to construct a gradient face. Then, the principal direction grouping strategy is introduced on the gradient face to characterize its high-order derivative features, and a principal direction feature map is formed according to the feature code of high-order derivatives direction changes in local neighborhood. Finally, block statistics and cascading of histogram features are made a vector to be input in to a support vector machine for multi-classification. Experimental results of several public face databases show that the proposed method is robust to changes in illumination,expression, and facial occlusion and has higher recognition efficiency.
作者
叶学义
王涛
应娜
钱丁炜
Ye Xueyi;Wang Tao;Ying Na;Qian Dingwei(School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2021年第10期1495-1503,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60802047,60702018).
关键词
人脸识别
局部导数模式
梯度脸
主方向分组策略
face recognition
local derivative pattern
gradient face
principal direction grouping strategy
