Details

Autor(en) / Beteiligte

• Zheng, Zhonglong
• Yu, Mudan
• Jia, Jiong
• Liu, Huawen
• Xiang, Daohong
• Huang, Xiaoqiao
• Yang, Jie

Titel

Fisher discrimination based low rank matrix recovery for face recognition

Ist Teil von

• Pattern recognition, 2014-11, Vol.47 (11), p.3502-3511

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2014

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In this paper, we consider the issue of computing low rank (LR) recovery of matrices with sparse errors. Based on the success of low rank matrix recovery in statistical learning, computer vision and signal processing, a novel low rank matrix recovery algorithm with Fisher discrimination regularization (FDLR) is proposed. Standard low rank matrix recovery algorithm decomposes the original matrix into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed FDLR executes low rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information are available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers and provides additional discriminating power over the standard low rank recovery models. The representative bases learned by the proposed method are encouraged to be closer within the same class, and as far as possible between different classes. Meanwhile, the sparse error recovered by FDLR is not discarded as usual, but treated as a feedback in the following classification tasks. Numerical simulations demonstrate that the proposed algorithm achieves the state of the art results. •A low rank matrix recovery algorithm with Fisher discrimination (FDLR) is proposed.•FDLR promotes the discrimination power in the learned representation dictionary.•FDLR is relaxed to be solved under augmented Lagrange multipliers framework.•A sparse error feedback strategy is presented and combined with FDLR.•FDLR shows robustness to severe occlusions of images in training and testing sets.