Tensor Sparse PCA and Face Recognition: A Novel Approach

TL;DR

This paper introduces a novel tensor sparse PCA approach combined with classification methods for face recognition, demonstrating improved accuracy over traditional PCA-based methods in experimental tests.

Contribution

It proposes a new tensor sparse PCA technique integrated with classification systems, showing enhanced face recognition accuracy compared to standard PCA methods.

Findings

Tensor sparse PCA improves face recognition accuracy.

Combination with specific classifiers outperforms traditional PCA.

Experimental results validate the effectiveness of the proposed method.

Abstract

Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.