DeXpression: Deep Convolutional Neural Network for Expression Recognition
This work addresses facial expression recognition for applications like human-computer interaction and safety systems, but it is incremental as it improves upon existing CNN approaches.
The authors tackled facial expression recognition by proposing a convolutional neural network (CNN) architecture, achieving accuracies of 99.6% on the CKP dataset and 98.63% on the MMI dataset, outperforming prior CNN-based state-of-the-art methods.
We propose a convolutional neural network (CNN) architecture for facial expression recognition. The proposed architecture is independent of any hand-crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches. We visualize the automatically extracted features which have been learned by the network in order to provide a better understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP) and MMI Facial Expression Databse are used for the quantitative evaluation. On the CKP set the current state of the art approach, using CNNs, achieves an accuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion recognition is 93.33%. The proposed architecture achieves 99.6% for CKP and 98.63% for MMI, therefore performing better than the state of the art using CNNs. Automatic facial expression recognition has a broad spectrum of applications such as human-computer interaction and safety systems. This is due to the fact that non-verbal cues are important forms of communication and play a pivotal role in interpersonal communication. The performance of the proposed architecture endorses the efficacy and reliable usage of the proposed work for real world applications.