Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild

Authors

  • Hanting Li University of Science and Technology of China
  • Hongjing Niu University of Science and Technology of China
  • Zhaoqing Zhu University of Science and Technology of China
  • Feng Zhao University of Science and Technology of China

DOI:

https://doi.org/10.1609/aaai.v37i1.25077

Keywords:

CMS: Affective Computing, CV: Object Detection & Categorization, CV: Video Understanding & Activity Analysis

Abstract

Compared with the image-based static facial expression recognition (SFER) task, the dynamic facial expression recognition (DFER) task based on video sequences is closer to the natural expression recognition scene. However, DFER is often more challenging. One of the main reasons is that video sequences often contain frames with different expression intensities, especially for the facial expressions in the real-world scenarios, while the images in SFER frequently present uniform and high expression intensities. Nevertheless, if the expressions with different intensities are treated equally, the features learned by the networks will have large intra-class and small inter-class differences, which are harmful to DFER. To tackle this problem, we propose the global convolution-attention block (GCA) to rescale the channels of the feature maps. In addition, we introduce the intensity-aware loss (IAL) in the training process to help the network distinguish the samples with relatively low expression intensities. Experiments on two in-the-wild dynamic facial expression datasets (i.e., DFEW and FERV39k) indicate that our method outperforms the state-of-the-art DFER approaches. The source code will be available at https://github.com/muse1998/IAL-for-Facial-Expression-Recognition.

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Published

2023-06-26

How to Cite

Li, H., Niu, H., Zhu, Z., & Zhao, F. (2023). Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild. Proceedings of the AAAI Conference on Artificial Intelligence, 37(1), 67-75. https://doi.org/10.1609/aaai.v37i1.25077

Issue

Section

AAAI Technical Track on Cognitive Modeling & Cognitive Systems