Tensor rank one differential graph preserving analysis for facial expression recognition

Authors:
Shuai Liu;Qiuqi Ruan;Chuantao Wang;Gaoyun An
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;School of Mechanical-electronic and Automobile Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China
Venue:
Image and Vision Computing
Year:
2012

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Abstract

This paper presents a new dimensionality reduction algorithm for multi-dimensional data based on the tensor rank-one decomposition and graph preserving criterion. Through finding proper rank-one tensors, the algorithm effectively enhances the pairwise inter-class margins and meanwhile preserves the intra-class local manifold structure. In the algorithm, a novel marginal neighboring graph is devised to describe the pairwise inter-class boundaries, and a differential formed objective function is adopted to ensure convergence. Furthermore, the algorithm has less computation in comparison with the vector representation based and the tensor-to-tensor projection based algorithms. The experiments for the basic facial expressions recognition show its effectiveness, especially when it is followed by a neural network classifier.