Facial Expression Recognition Using ROI KNN Deep Convolutional Neural Networks
Deep neural networks have been proved to be able to mine distributed representation of data including image, speech and text. By building two models of deep convolutional neural networks and deep sparse rectifier neural networks on facial expression dataset, we make contrastive evaluations in facial expression recognition system with deep neural networks. Additionally, combining region of interest (ROI) and K nearest neighbors (KNN) we propose a fast and simple improved method called "ROI KNN" for facial expression classification, which relieves the poor generalization of deep neural networks due to lacking of data and decreases the testing error rate apparently and generally. The proposed method also improves the robustness of deep learning in facial expression classification.