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arXiv · 1912.09970

Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition

Meixiang Zhao·Zhigang Jia·Yunfeng Cai·Xiao Chen·Dunwei Gong
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Abstract

The two-dimensional principal component analysis (2DPCA) has become one of the most powerful tools of artificial intelligent algorithms. In this paper, we review 2DPCA and its variations, and propose a general ridge regression model to extract features from both row and column directions. To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model. R2DPCA generates a weighting vector with utilizing the label information, and maximizes a relaxed criterion with applying an optimal algorithm to get the essential features. The R2DPCA-based approaches for face recognition and image reconstruction are also proposed and the selected principle components are weighted to enhance the role of main components. Numerical experiments on well-known standard databases indicate that R2DPCA has high generalization ability and can achieve a higher recognition rate than the state-of-the-art methods, including in the deep learning methods such as CNNs, DBNs, and DNNs.

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Meixiang Zhao, Zhigang Jia, Yunfeng Cai, Xiao Chen, Dunwei Gong. 2019-12-19. Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition. https://arxiv.org/abs/1912.09970

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