基于加权紧凑局部图结构的人脸识别算法

Face recognition algorithm based on weighted compact local graph structure

针对局部图结构算法(local graph structure,LGS)构建图结构时用到的像素点距离中心像素太远,以及在图结构形成后分配权重时没有结合周围像素点到中心像素的距离因素问题,提出加权紧凑局部图结构(weighted compact local graph structure,WCLGS)算法。该算法定义了一种混合特征提取策略,从四个方向为中心像素点构建图结构,分别在垂直方向和对角线方向捕获对称和非对称信息,并且在图结构形成后对距中心像素点近的边赋较大的权重,对距中心像素点远的边赋较小的权重。WCLGS通过提取更近的像素点信息和合理的加权策略,密切关注中心像素点的近邻元素的差异,使得中心点两侧的信息提取更加均匀充分。实验证明,与现有的一些局部图结构算法相比,WCLGS在ORL(Olivetti Research Laboratory)、AR(active record)和HD(high definition)热红外人脸数据库上有更好的识别率和性能。

LGS algorithm extracts information from far pixels.It assigns weights without considering distance between surrounding pixels and center pixel.Thus,this paper proposed WCLGS.The algorithm defined a hybrid feature extract strategy.It constructed graph structure from four directions of central pixel.The structure caught symmetric and asymmetric information in vertical direction and diagonal direction respectively.WCLGS gave a larger weight to edges close to central pixel,and a smaller weight to edges far from central pixel.It paid close attention to differences of near neighbor elements and center pixel by extracting closer pixel information and reasonable weighting strategy.Information extracted on both sides of center point was more uniform and sufficient.Compared with existing local graph structure algorithms,WCLGS has better recognition rate and performance in ORL,AR and HD infrared face databases.