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Abstract

Even though 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 work, we carry out a study of multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduce a model by classification task. Our model was evaluated on the FER2013 dataset and achieved a comparable performance to the current state-of-the-art methods.

Item Type:	Conference Publication
Authors/Creators:	Nguyen, H-D and Yeom, S and Oh, I-S and Kim, K-M and Kim, S-H
Keywords:	facial expression recognition in the wild, multi-level convolutional neural networks
Journal or Publication Title:	Proceedings from the International Conference on Pattern Recognition and Artificial Intelligence
Publisher:	Centre for Pattern Recognition and Machine Intelligence
Copyright Information:	Copyright CENPARMI 2018
Related URLs:	Google Scholar: Yeom, S UTAS Profile: Yeom, S
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