MPCA: Multilinear Principal Component Analysis of Tensor Objects

• Published in: IEEE transaction on neural networks and learning systems 2008-01, Vol.19 (1), p.18-39
• Main Authors: Haiping Lu, Plataniotis, K.N., Venetsanopoulos, A.N.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; Biometrics; Computer science; control theory; systems; Computer vision; Connectionism. Neural networks; Dimensionality reduction; Exact sciences and technology; Face recognition; Feature extraction; Gait; gait recognition; Humans; Mathematical analysis; Mathematical models; multilinear principal component analysis (MPCA); Neural Networks (Computer); Object recognition; Pattern recognition; Pattern Recognition, Automated - methods; Pattern Recognition, Visual - physiology; Pattern recognition. Digital image processing. Computational geometry; Principal Component Analysis; Principal components analysis; Projection; Strontium; Studies; Tensile stress; tensor objects; Tensors; Two dimensional; Video sequences
• ISSN: 1045-9227; 2162-237X; 1941-0093; 2162-2388
• DOI: 10.1109/TNN.2007.901277

This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2D/3D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2D variant (2D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA's utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.