HERO: Human Emotions Recognition for Realizing Intelligent Internet of Things

Abstract

Human emotions recognition (HERO) is considered as one of the important techniques for realizing the intelligent Internet of Things. The demand for a robust and precise facial expression recognition algorithm is urgent for the HERO. In this paper, we propose a deep recognition algorithm based on the ensemble deep learning model. The proposed algorithm consists of three sub-networks with different depths. Each sub-network is comprised of convolutional neutral networks and trained independently. The sub-network with more convolutional layers recognizes emotions by extracting local details such as the features of eyes and mouth, while the sub-network with less convolutional layers focuses on the macrostructure of the input image. The three sub-networks are assembled together to constitute the whole model. The experiment is based on the Kaggle facial expression recognition challenge database (FER2013), the Japanese female facial expression database, and the AffectNet database. The experimental results show that the proposed algorithm achieves a test accuracy of 71.91%, 96.44%, and 62.11% better than other competitors, and increases the test accuracy by approximately 2-3% than unique sub-networks.