Systems Engineering and Electronics
• SOFTWARE ALGORITHM AND SIMULATION •
Chongsheng Zhang1, Pengyou Wang1, Ke Chen2,*, and Joni-Kristian K¨am¨ ar¨ainen2
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+).
Chongsheng Zhang, Pengyou Wang, Ke Chen, and Joni-Kristian K¨am¨ ar¨ainen. Identity-aware convolutional neural networks for facial expression recognition[J]. Systems Engineering and Electronics, doi: 10.21629/JSEE.2017.04.18.
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