Sparse localized facial motion dictionary learning for facial expression recognition

This paper presents a new framework for facial motion modeling with applications to facial expression recognition. First, we design sparse localized facial motion dictionaries from dense motion flow data of facial expression image sequences. Regularization based on spatial localized support map in addition to the sparsity constraints enables spatially localized dictionary learning. Proposed localized dictionaries are effective for local facial motion description as well as global facial motion analysis. Experimental results using CK+ database shows promising results for automatic facial expression recognition from motion flow data.