基于子模式的加权邻域极大边界准则的人脸识别

摘要

本文提出了一种基于子模式的加权邻域极大边界准则的人脸识别算法.该方法首先在训练过程中对人脸图像进行子块划分,采用邻接点的类信息自适应地计算每块的权重以提高人脸在姿态、表情以及光照等变化下的鲁棒性.其次,对每块图像采用加权邻域极大边界准则进行特征提取,该准则充分利用了数据的类信息,选择数据的邻域点最优重构系数用在目标函数中,保留了数据的局部几何结构,从而在低维空间中提取出更好的分类特征.最后在识别过程中,融合待识别图像在各子块中与训练图像的相似度进行识别.实验结果表明,本文算法能够有效地提取局部特征,较好地保留原始数据的非线性结构,较其他全局方法如主成分分析方法,线性判别式方法和加权邻域极大边界准则算法具有更好的识别性能.%A Subpattern-based Weighted Neighborhood Maximum Margin Criterion (SP-WNMMC) algorithm is proposed for face recognition. In order to enhance the robustness to facial pose, expression and illumination variations,SP-WNMMC method firstly operates on sub-patterns partitioned from an original whole face image. The contribution of each sub-pattern can be adaptively computed through the class information of neighborhood. Secondly, WNMMC is adopted in each sub-pattern to extract local features. WNMMC can preserve the local geometric structure of database. The objective function of WNMMC leads to the enhancement of classification capacity by using the linear reconstruction coefficients. Thirdly, for a new face image to be recognized, all the likelihoods in all the subpattems are fused together for the final recognition result. Experiments show that our method can effectively extract the local feature while preserving the non-linear structures in sub-pattern sets. It can consistently outperform other recognition methods based on Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and WNMMC.