Deep Dive into Faces: Pose & Illumination Invariant MultiFace Emotion Recognition System 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 realtime 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.

RealTime Facial Affective Computing on Mobile Devices Convolutional Neural Networks (CNNs) have become one of the stateoftheart methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for realtime facial affective computing tasks. We propose a lightweight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with stateoftheart methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a realtime facial affective computing application on an actual mobile device.