Cross-Dataset Facial Expression Recognition based on Arousal-Valence Emotion Model and Transfer Learning Method

DOI

10.2991/mecae-17.2017.24How to use a DOI?

Keywords

Facial expression recognition; Arousal-valence emotion dimensions; TPCA; Fusion.

Abstract

Traditional facial expression recognition methods assume that facial expression in the training and testing sets are collected under the same condition such that they are independent and identically distributed. However, the assumption is not satisfied in many real applications. This problem is referred to as cross-dataset facial expression recognition. On the other hand, the traditional facial expression recognition methods are based on basic emotion theory proposed by Ekman. Unfortunately, the theory is limited to express diverse and subtle emotion. To solve the problem of the cross-dataset facial expression recognition and enrich the emotion expression, a transfer learning algorithm TPCA and arousal-valence emotional model are adopted in this paper. A new facial emotion recognition method based on TPCA and two-level fusion is proposed, which combine weight fusion and correlation fusion between arousal and valence to improve the recognition performance under cross-dataset scenarios. The contrast experimental results show that the proposed method can get better recognition result than the traditional methods.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yong Yang
AU  - Chuan Liu
AU  - Qingshan Wu
PY  - 2017/03
DA  - 2017/03
TI  - Cross-Dataset Facial Expression Recognition based on Arousal-Valence Emotion Model and Transfer Learning Method
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 132
EP  - 138
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.24
DO  - 10.2991/mecae-17.2017.24
ID  - Yang2017/03
ER  -