Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138945
Title: Facial motion prior networks for facial expression recognition
Authors: Chen, Yuedong
Wang, Jianfeng
Chen, Shikai
Shi, Zhongchao
Cai, Jianfei
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Chen, Y., Wang, J., Chen, S., Shi, Z., & Cai, J. (2019). Facial motion prior networks for facial expression recognition. 2019 IEEE Visual Communications and Image Procensing (VCIP). doi:10.1109/VCIP47243.2019.8965826
Abstract: Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.
URI: https://hdl.handle.net/10356/138945
ISBN: 9781728137230
DOI: 10.1109/VCIP47243.2019.8965826
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/VCIP47243.2019.8965826
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IMI Conference Papers

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