title: Block-wise two-dimensional maximum margin criterion for face recognition.
abstract: Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach for MMC, to deal with matrix representation data, that is, images. The proposed method, namely, block-wise two-dimensional maximum margin criterion (B2D-MMC), aims to find local subspace projections using unilateral matrix multiplication in each block set, such that in the subspace a block is close to those belonging to the same class but far from those belonging to different classes. B2D-MMC avoids iterations and alternations as in current bilateral projection based two-dimensional feature extraction techniques by seeking a closed form solution of one-side projection matrix for each block set. Theoretical analysis and experiments on benchmark face databases illustrate that the proposed method is effective and efficient.
authors.affiliation: School of Computer Science, Dongguan University of Technology, Dongguan 523808, China.
authors.forename: Xiao-Zhang
authors.initials: XZ
authors.lastname: Liu
authors.affiliation: School of Computer Science, Zhongyuan University of Technology, Zhengzhou 450007, China.
authors.forename: Guan
authors.initials: G
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D005145:Face; D005149:Facial Expression; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results