How Deep Can We Rely on Emotion Recognition

The emerging success of digital social media has had an impact on several fields ranging from science to economy and business. Therefore, there is an invested interest in emotion detection and recognition technology from facial expressions, in order to increase their market competitiveness. This area still presents many challenges, namely the difficulty in achieving real-time facial recognition. Herein we tackle this problem by crossing methods targeting both static images and active images. In this work, we explore the recent technological breakthroughs in deep learning and develop a system based on automatic recognition of human face expressions using Convolutional Neural Networks (CNN). We use the Cohn-Kanade Extended (CKP) dataset for testing our proposed CNN model along with an augmented version, which demonstrated effectiveness in seven basic expressions. In order to enhance the quality of the results instead of the overlapping method for building the augmented dataset we propose random perturbations from a wide set including: skew, translation, scale, and horizontal flip. Moreover, we built a real-time video framework using our model (a version of LeNet-5) which is fed with frames detected with Viola-Jones face tracker that reproduce the CKP dataset. The results are promising.