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  • Our paper has been accepted for publication in Pattern Recognition Letters.
  • Our paper has been accepted for publication in Pattern Recognition Letters.

    One of our papers has been accepted for publication in Pattern Recognition Letters.

    Bibliographic Information
    Bo Yang, Jianming Wu, Kazushi Ikeda, Gen Hattori, Masaru Sugano, Yusuke Iwasawa, Yutaka Matsuo. “Face-mask-aware Facial Expression Recognition based on Face Parsing and Vision Transformer”, Pattern Recognition Letters, In Press, Pre-proof (2022).
    https://doi.org/10.1016/j.patrec.2022.11.004
    Overview
    As wearing face masks is becoming an embedded practice due to the COVID-19 pandemic, facial expression recognition (FER) that takes face masks into In this paper, we propose a face parsing and vision transformer-based method to improve the accuracy of First, in order to improve the precision of distinguishing the unobstructed facial region as well as those parts of the face covered by a mask, we re-train First, in order to improve the precision of distinguishing the unobstructed facial region as well as those parts of the face covered by a mask, we re-train a face-mask-aware face parsing model, based on the existing face parsing dataset automatically relabeled with a face mask and pixel Second, we propose a vision transformer with a cross attention mechanism-based FER classifier, capable of taking both occluded and non-occluded Second, we propose a vision Transformer with a cross attention mechanism-based FER classifier, capable of taking both occluded and non-occluded facial regions into account and reweigh these two parts automatically to get the best facial expression recognition performance. The proposed method outperforms existing state-of-the-art face-mask-aware FER methods, as well as other occlusion-aware FER methods, on two datasets that contain three The proposed method outperforms existing state-of-the-art face-mask-aware FER methods, as well as other occlusion-aware FER methods, on two datasets that contain three kinds of emotions (M-LFW-FER and M-KDDI-FER datasets) and two datasets that contain seven kinds of emotions (M-FER-2013 and M-CK+ datasets).