Hierarchical uncorrelated multiview discriminant locality preserving projection for multiview facial expression recognition

Existing multi-view facial expression recognition algorithms are not fully capable of finding discriminative directions if the data exhibits multi-modal characteristics. This research moves toward addressing this issue in the context of multi-view facial expression recognition. For multi-modal data, local preserving projection (LPP) or local Fisher discriminant analysis (LFDA)-based approach is quite appropriate to find a discriminative space. Also, the classification performance can be enhanced by imposing uncorrelated constraint onto the discriminative space. So for multi-view (multi-modal) data, we proposed an uncorrelated multi-view discriminant locality preserving projection (UMvDLPP)-based approach to find an uncorrelated common discriminative space. Additionally, the proposed UMvDLPP is implemented in a hierarchical fashion (H-UMvDLPP) to obtain an optimal performance. Extensive experiments on BU3DFE dataset show that UMvDLPP performs slightly better than the existing methods. However, an improvement of approximately 3% as compared to the existing state-of-the-art multi-view learning-based approaches is achieved by our H-UMvDLPP. This improvement is due to the fact that the proposed method enhances the discrimination between the classes more effectively, and classifies expressions category-wise followed by classification of the basic expressions embedded in each of the subcategories (hierarchical approach).