Abstract

A facial expression is a combination of an expressive component and a neutral component of a person. In this paper, we propose to recognize facial expressions by extracting information of the expressive component through a de-expression learning procedure, called De-expression Residue Learning (DeRL). First, a generative model is trained by cGAN. This model generates the corresponding neutral face image for any input face image. We call this procedure de-expression because the expressive information is filtered out by the generative model; however, the expressive information is still recorded in the intermediate layers. Given the neutral face image, unlike previous works using pixel-level or feature-level difference for facial expression classification, our new method learns the deposition (or residue) that remains in the intermediate layers of the generative model. Such a residue is essential as it contains the expressive component deposited in the generative model from any input facial expression images. Seven public facial expression databases are employed in our experiments. With two databases (BU-4DFE and BP4D-spontaneous) for pre-training, the DeRL method has been evaluated on five databases, CK+, Oulu-CASIA, MMI, BU-3DFE, and BP4D+. The experimental results demonstrate the superior performance of the proposed method.

Department(s)

Computer Science

International Standard Book Number (ISBN)

978-153866420-9

International Standard Serial Number (ISSN)

1063-6919

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

14 Dec 2018

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