Tensor-Based Feature Representation With Application To Multimodal Face Recognition

In this paper, a novel feature representation to multimodal face recognition is proposed, which possesses three properties: completeness, robustness and compactness. This feature descriptor allows all information of an object to be reproduced and its representation is invariant to rigid motion. In order to effectively take advantage of the proposed feature descriptor, we amend our previous ND-PCA scheme with multidirectional decomposition technique, and provide the estimation of the upper bound error of the amended classifier. It is proved to be linear optimal compared to other linear classifiers. To investigate the numerical performance of the presented feature descriptor, we apply it to both multiple modal and single modal samples, and the revised ND-PCA classifier is performed on the resulting feature representations. The experiments of verification and identification are carried out on two different gallery-probe face databases in order for the results to be evaluated by ROC and CMC curves independently.