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Deep convolutional BiLSTM fusion network for facial expression recognition

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Abstract

Deep learning algorithms have shown significant performance improvements for facial expression recognition (FER). Most deep learning-based methods, however, focus more attention on spatial appearance features for classification, discarding much useful temporal information. In this work, we present a novel framework that jointly learns spatial features and temporal dynamics for FER. Given the image sequence of an expression, spatial features are extracted from each frame using a deep network, while the temporal dynamics are modeled by a convolutional network, which takes a pair of consecutive frames as input. Finally, the framework accumulates clues from fused features by a BiLSTM network. In addition, the framework is end-to-end learnable, and thus temporal information can be adapted to complement spatial features. Experimental results on three benchmark databases, CK+, Oulu-CASIA and MMI, show that the proposed framework outperforms state-of-the-art methods.

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Authors and Affiliations

  1. School of Electronic and Control Engineering, Chang’an University, Middle of the South Second Ring Road, Xi’an, 710064, Shanxi, China

    Dandan Liang, Huagang Liang & Yipu Zhang

  2. B-DAT, School of Information and Control, Nanjing University of Information Science and Technology, 219 Nanjin Road, Nanjing, 210044, Jiangsu, China

    Zhenbo Yu

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  1. Dandan Liang
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  2. Huagang Liang
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Correspondence to Dandan Liang.

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Liang, D., Liang, H., Yu, Z. et al. Deep convolutional BiLSTM fusion network for facial expression recognition. Vis Comput 36, 499–508 (2020). https://doi.org/10.1007/s00371-019-01636-3

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