Dictionary learning for sparse representation classifiers (SRC) has demonstrated great success for many classification problems, i.e., face recognition, object detection, etc. However, it has not enjoyed a similar reception in the facial expression recognition literature. In this paper, we applied dictionary learning methods to the task of facial expression recognition, which is then compared with SVM. In addition, we introduce a new dictionary learning method that incorporates side information, which is contained in the training data but not available in the testing phase. In particular, we introduce a new soft constraint derived from side information and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution to the objective function is efficiently obtained using the K-SVD algorithm. Our algorithm learns the dictionary and an optimal linear classifier jointly. Experimental part demonstrates the effectiveness of the sparse representation classifiers for facial expression recognition problem, and dictionary learning with side information method achieves further improvement on low resolution facial expression recognition.
展开▼
