Emotion recognition plays an indispensable role in human-machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

Item Type:	Refereed Article
Keywords:	ensemble model, facial emotion recognition in the wild, multi-level convolutional neural networks
Research Division:	Information and Computing Sciences
Research Group:	Computer vision and multimedia computation
Research Field:	Pattern recognition
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Yeom, S (Dr Soonja Yeom)
ID Code:	132151
Year Published:	2019
Web of Science® Times Cited:	11
Deposited By:	Information and Communication Technology
Deposited On:	2019-04-24
Last Modified:	2020-05-18
Downloads:	0