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<p>Existing facial expression recognition (FER) methods are mainly devoted to learning discriminative features from normal-light images. However, their performance drops sharply when they are used for low-light images. In this paper, we propose a novel low-light FER framework (termed LL-FER) that can simultaneously enhance the images and recognition tasks of low-light facial expression images. Specifically, we first meticulously design a low-light enhancement network (LLENet) to recover expressions images’ rich detail information. Then, we design a joint loss to train the LLENet with FER network in a cascade manner, so that the FER network can guide the LLENet to gradually perceive and restore discriminative features which are useful for FER during the training process. Extensive experiments show that the LLENet not only achieves competitive results both quantitatively and qualitatively, but also in the LL-FER framework, which can produce results more suitable for FER tasks, further improving the performance of the FER methods.</p>

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A joint learning method for low-light facial expression recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月9日

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  • Yuanlun Xie,
  • Jie Ou,
  • Bihan Wen,
  • Zitong Yu,
  • Wenhong Tian

摘要

Existing facial expression recognition (FER) methods are mainly devoted to learning discriminative features from normal-light images. However, their performance drops sharply when they are used for low-light images. In this paper, we propose a novel low-light FER framework (termed LL-FER) that can simultaneously enhance the images and recognition tasks of low-light facial expression images. Specifically, we first meticulously design a low-light enhancement network (LLENet) to recover expressions images’ rich detail information. Then, we design a joint loss to train the LLENet with FER network in a cascade manner, so that the FER network can guide the LLENet to gradually perceive and restore discriminative features which are useful for FER during the training process. Extensive experiments show that the LLENet not only achieves competitive results both quantitatively and qualitatively, but also in the LL-FER framework, which can produce results more suitable for FER tasks, further improving the performance of the FER methods.