Hybrid deep neural networks for face emotion recognition

Deep Neural Networks (DNNs) outperform traditional models in numerous optical recognition missions containing Facial Expression Recognition (FER) which is an imperative process in next-generation Human-Machine Interaction (HMI) for clinical practice and behavioral description. Existing FER methods do not have high accuracy and are not sufficient practical in real-time applications. This work proposes a Hybrid Convolution-Recurrent Neural Network method for FER in Images. The proposed network architecture consists of Convolution layers followed by Recurrent Neural Network (RNN) which the combined model extracts the relations within facial images and by using the recurrent network the temporal dependencies which exist in the images can be considered during the classification. The proposed hybrid model is evaluated based on two public datasets and Promising experimental results have been obtained as compared to the state-of-the-art methods.