Facial Expression Recognition using Convolution Neural Network Enhancing with Pre-processing Stages

Recognizing human expression is one of the most popular problems in the Human-Computer Interaction field. Facial Expression Recognition present a great challenge in a wide variety of areas due to varying conditions of the image, which influences expression recognition and makes this task a complex problem. The main difficulties depend on the irregular nature of the human face and the different conditions such as orientation, light and shadows. Lately, Deep learning obtained more attention as an intelligent technology to achieve robustness and offer best performance of expression recognition. Further investigations are still needed in this field in order to make the recognition process very efficient. For that, we present in this paper a new Convolutional Neural Networks model enhancing with pre-processing stages to recognize seven classes (six basic expressions and one neutral). Our approach contains two phases: normalization, and expression recognition. The result can achieve high accuracy compared to recent works with the popular facial expression databases such as CK+, JAFFE, and FER-2013.