Identity-aware convolutional neural networks for facial expression recognition

Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific characteristics of facial expressions.To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face...

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Bibliographic Details
Published in:Journal of systems engineering and electronics 2017-08, Vol.28 (4), p.784-792
Main Authors: Chongsheng Zhang, Pengyou Wang, Ke Chen, Joni-Kristian Kamarainen
Format: Article
Language:English
Subjects:
expression
facial
learning
classification
identity-aware
recognition
deep
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Summary:Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific characteristics of facial expressions.To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches,can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).
ISSN:1004-4132
DOI:10.21629/JSEE.2017.04.18