Identity-aware convolutional neural networks for facial expression recognition

42Citations
Citations of this article
44Readers
Mendeley users who have this article in their library.

Abstract

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+).

Cite

CITATION STYLE

APA

Zhang, C., Wang, P., Chen, K., & Kämäräinen, J. K. (2017). Identity-aware convolutional neural networks for facial expression recognition. Journal of Systems Engineering and Electronics, 28(4), 784–792. https://doi.org/10.21629/JSEE.2017.04.18

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free