基于全局和分离部件融合的双L1稀疏表示人脸图像识别算法

考虑到人脸识别中全局与局部信息的互补作用,提出基于全局和分离部件相结合的双L1稀疏表示人脸图像识别算法.首先在L1稀疏表示的基础上,对入脸进行全局稀疏逼近.其次,在分离部件识别模型中,抽取并对齐稍有重叠的几个人脸部件,分别进行稀疏表示,然后使用基于稀疏表示残差的相似度投票方法,将各部件逼近结果综合.最后在决策层上将全局与部件的稀疏表示加权集成,形成双L1稀疏表示分类器.在公用人脸数据库上的实验表明,集成分类器优于各单一模块的识别性能,且由于融合了对光照、表情等变化不敏感的部件信息,系统鲁棒性得到提高.

Bi-L1 Sparse Representation Algorithm for Face Recognition Based on Fusion of Global and Separated Components

Considering the complementation of global and local components,bi-L1 sparse representation algorithm for face recognition based on fusion of global and separated components is proposed.Firstly,based on L1 sparse representation,the global information is used to obtain the global sparse approximation.Then,several slightly overlapping face components are extracted and aligned in the recognition model of separated components.After that,the sparse representation of all the components is obtained respectively.The sparse approximation results of each component are combined with a similarity voting method based on the residuals of class representation.Finally,the weighted integration of the global and components sparse representation is used to construct the bi-L1 sparse representation classifier in decision-making layer.The experimental results on public available database demonstrate that the performance of the integration classifier is superior to that of each single module.Due to the fusion of component information which is insensitive to variation of illumination and expression etc.,the robustness of the system is enhanced.