Half-Face Dictionary Integration for Representation-Based Classification
提出半脸字典集成算法，通过生成虚拟半脸样本扩充训练数据，并消除冗余原子，最后加权融合不同字典的重构残差，提升小样本下的人脸识别效果。
This paper presents a half-face dictionary integration (HFDI) algorithm for representation-based classification. The proposed HFDI algorithm measures residuals between an input signal and the reconstructed one, using both the original and the synthesized dual-column (row) half-face training samples. More specifically, we first generate a set of virtual half-face samples for the purpose of training data augmentation. The aim is to obtain high-fidelity collaborative representation of a test sample. In this half-face integrated dictionary, each original training vector is replaced by an integrated dual-column (row) half-face matrix. Second, to reduce the redundancy between the original dictionary and the extended half-face dictionary, we propose an elimination strategy to gain the most robust training atoms. The last contribution of the proposed HFDI method is the use of a competitive fusion method weighting the reconstruction residuals from different dictionaries for robust face classification. Experimental results obtained from the Facial Recognition Technology, Aleix and Robert, Georgia Tech, ORL, and Carnegie Mellon University-pose, illumination and expression data sets demonstrate the effectiveness of the proposed method, especially in the case of the small sample size problem.