Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition
This work addresses face recognition, a domain-specific problem, with incremental improvements over existing 2DPCA variations.
The paper tackles face recognition by proposing a novel relaxed 2DPCA (R2DPCA) method that enhances generalization and feature extraction, achieving higher recognition rates than state-of-the-art methods including deep learning approaches on standard databases.
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.