Sparsity preserving multiple canonical correlation analysis with visual emotion recognition to multi-feature fusion

Sparsity preserving projections (SPP) aim to preserve the sparse reconstructive relationship among the data and have been successfully applied to face recognition. The projections are invariant to rotations, rescalings, and translations of the data, and more importantly, they contain natural discriminating information even without class labels. Based on the concept of SSP, it presents a new method for multi-feature information fusion based on the Sparsity Preserving Multiple Canonical Correlation Analysis (SPMCCA), which can preserve the sparse reconstructive relationship of the data for recognition from multi-feature information representation. We implement a prototype of SPM-CCA with the application to visual-based human emotion recognition. Experimental results show that the proposed method outperforms the traditional methods of serial fusion, Canonical Correlation Analysis (CCA), Multiple Canonical Correlation Analysis (MCCA) and recently proposed Sparsity Preserving Canonical Correlation Analysis (SPCCA).