Deep Dive into Faces: Pose & Illumination Invariant Multi-Face Emotion Recognition System

Abstract

One of the advancements in humanization of robots is its ability to recognize human emotions. Facial expression plays a key role in identifying human emotions relative to other cues. In this research, an intelligent network capable of real-time emotion recognition from multiple faces using deep learning technique is presented. The proposed network is based on Convolution Neural Network (CNN) in which three blocks of Convolution layers for feature extraction and two blocks of Dense layers for classification are used. The novelty of this method lies in recognizing emotions from multiple faces simultaneously in real time and its invariance to head pose, illumination and age factor. Most of reported work in literature for multiple faces is for frontal face without illumination variation. The proposed emotion recognition system is deployed on Raspberry Pi3 B+ for human robot interaction applications and achieved an average accuracy of 95.8% in real time.