EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection

Wei Li; Farnaz Abtahi; Zhigang Zhu; Lijun Yin

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arXiv · 1702.02925

EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection

Wei Li·Farnaz Abtahi·Zhigang Zhu·Lijun Yin

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Abstract

In this paper, we propose a deep learning based approach for facial action unit detection by enhancing and cropping the regions of interest. The approach is implemented by adding two novel nets (layers): the enhancing layers and the cropping layers, to a pretrained CNN model. For the enhancing layers, we designed an attention map based on facial landmark features and applied it to a pretrained neural network to conduct enhanced learning (The E-Net). For the cropping layers, we crop facial regions around the detected landmarks and design convolutional layers to learn deeper features for each facial region (C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and Cropping (EAC) Net, which can learn both feature enhancing and region cropping functions. Our approach shows significant improvement in performance compared to the state-of-the-art methods applied to BP4D and DISFA AU datasets.

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BibTeX RIS

Wei Li, Farnaz Abtahi, Zhigang Zhu, Lijun Yin. 2017-02-09. EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection. https://arxiv.org/abs/1702.02925

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