Meaningful Learning for Deep Facial Emotional Features

Facial expression is an important aspect to recognize emotions between humans. However, this task remains difficult for machines. Several approaches have been developed aiming at strengthening the machine and endowing it, with the ability to decipher and read people’s emotions from their faces in order to interact more intelligently. In this context, many deep learning (DL) approaches have been applied due to their outstanding recognition Accuracy. Aiming to gain better performance for facial expression recognition (FER) systems, we propose a hybrid DL architecture based on convolutional neural network and Stacked AutoEncoder. The main idea of this work is to combine the two feature vectors generated by each of these architectures and feed the resulting vector to a meaningful neural network. The architecture of the latter leads to learn each feature by dedicating a set of neurons for each component of the vector before combining them all together in the last layer. The publicly available four facial expression Datasets: Japanse Female Facial Expression (JAFFE), Extended Chon-Kanade (CK+), Facial Expression Recognition 2013 (FER2013) and AffectNet, were used during this research for both training and testing. The experimental results of our proposed architecture are comparable to or better than the relevant state-of-the-art methods in term of Accuracy, Recall, Precision and F-measure. We noted that our proposed approach obtains the best accuracy of 98.65% on the CK+, 95.78% on the JAFFE, 63.14% on the AffectNet and 80.02% on the FER2013 Datasets.