Enhancing CNN with Preprocessing Stage in Automatic Emotion Recognition

Abstract

Emotion recognition from facial expression is the subfield of social signal processing which is applied in wide variety of areas, specifically for human and computer interaction. Many researches have been proposed for automatic emotion recognition, which is fundamentally using machine learning approach. However, recognizing basic emotions such as angry, happy, disgust, fear, sad, and surprise is still becoming a challenging problem in computer vision. Lately, deep learning has gained more attention to solve many real-world problems, including emotion recognition. In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some preprocessing methods to show the influences of its in CNN performance. The compared data preprocessing methods are: resizing, face detection, cropping, adding noises, and data normalization consists of local normalization, global contrast normalization and histogram equalization. Face detection as single pre-processing phase achieved significant result with 86.08 % of accuracy, compared with another pre-processing phase and raw data. However, by combining those techniques can boost performance of CNN and achieved 97.06% of accuracy.