Synthetic Sentiment Energy: Situation Assessment in Crowds Group Emotion Recognition has been recognized as a difficult task because of the difficulty in defining identification elements, the overlap of people, and the differences of individuals. This paper first discusses the two elements required for group emotion recognition: The facial expression on each personxe2x80x99s face and the position of each personxe2x80x99s face in the crowd. Then, it design BinaryChannel CNN and Yolo V3 respectively as a strategy to obtain these two kinds of information. This paper propose a Sentiment Energy Function to measure group sentiments, which consists of three components: Expression energy, Expression Change Rate Energy and Displacement energy. These three components were fused aiming to achieve the group emotion energy. Through experiment methods, it is clear that the improved BinaryChannel CNN in this paper perform good in facial expression recognition. The group emotion recognition system designed has comparatively higher robustness and adaptability. The results obtained are consistent with the results of human analysis.

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.