Multi-view Facial Expressions Recognition using Local Linear Regression of Sparse Codes
نویسندگان
چکیده
We introduce a linear regression-based projection for multi-view facial expressions recognition (MFER) based on sparse features. While facial expression recognition (FER) approaches have become popular in frontal or near to frontal views, few papers demonstrate their results on arbitrary views of facial expressions. Our model relies on a new method for multi-view facial expression recognition, where we encode appearance-based facial features using sparse codes and learn projections from nonfrontal to frontal views using linear regression projection. We then reconstruct facial features from the projected sparse codes using a common global dictionary. Finally, the reconstructed features are used for facial expression recognition. Our regression of sparse codes approach outperforms the state-of-theart results on both protocols of BU3DFE dataset.
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