D-FES: Deep facial expression recognition system
Shikhar Sharma, Krishan Kumar, Navjot Singh · 2017
In this computer era, the most interesting thing is to determine the human emotions with machines. Human relies on emotion for conveying their response to others. With advancement in the field of artificial intelligence, machines are also learning to understand these human emotions. With the evolution of deep learning in the field of computer vision, computers are used for solving problems like object detection, motion recognition, anomaly detection, and video surveillance. With our work, we tried to employ same deep learning methods to tackle the problem of emotion detection. Some of the previous works explored the capabilities of deep learning and achieved the efficiency like never before. Therefore, to make the interaction between human and computer more realistic we trained the machine to understand non-verbal communication in form of emotions. In this paper, we used a novel approach to detect the emotion based on the lips structure over the period of time. The use of the recurrent neural network to analyze the pattern with time provided the classification of emotions into 6 different classes. The qualitative, as well as quantitative evaluation, is done in order to compare the performances of our proposed model and state-of-the-art models. D-FES can perform in the real-time environment for accurate tracking and classification of emotions.