Multi-Pose Facial Expression Recognition Based on Generative Adversarial Network

IEEE Access

卷:7

Jia Deng¹  Dejian Li²  Ruiming Luo²  Zejian Li²  Shouqian Sun² 

[1] State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China;

[2] Zhejiang Key Laboratory of Design and Intelligence and Digital Creativity, School of Computer Science, Zhejiang University, Hangzhou, China;

关键词: Facial expression recognition;    computer vision;    image analysis;    convolutional neural networks;    multi-pose;    generative adversarial network;   

DOI  :  10.1109/ACCESS.2019.2945423

来源: DOAJ

【 摘 要 】

The recognition of human emotions from facial expression images is one of the most important topics in the machine vision and image processing fields. However, recognition becomes difficult when dealing with non-frontal faces. To alleviate the influence of poses, we propose an encoder-decoder generative adversarial network that can learn pose-invariant and expression-discriminative representations. Specifically, we assume that a facial image can be divided into an expressive component, an identity component, a head pose component and a remaining component. The encoder encodes each component into a feature representation space and the decoder recovers the original image from these encoded features. A classification loss on the components and an ℓ₁ pixel-wise loss are applied to guarantee the rebuilt image quality and produce more constrained visual representations. Quantitative and qualitative evaluations on two multi-pose datasets demonstrate that the proposed algorithm performs favorably compared to state-of-the-art methods.

【 授权许可】

Unknown