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Deep learning the dynamic appearance and shape of facial action units

Jaiswal, Shashank; Valstar, Michel F.


Shashank Jaiswal

Michel F. Valstar


Spontaneous facial expression recognition under uncontrolled conditions is a hard task. It depends on multiple factors including shape, appearance and dynamics of the facial features, all of which are adversely affected by environmental noise and low intensity signals typical of such conditions. In this work, we present a novel approach to Facial Action Unit detection using a combination of Convolutional and Bi-directional Long Short-Term Memory Neural Networks (CNN-BLSTM), which jointly learns shape, appearance and dynamics in a deep learning manner. In addition, we introduce a novel way to encode shape features using binary image masks computed from the locations of facial landmarks. We show that the combination of dynamic CNN features and Bi-directional Long Short-Term Memory excels at modelling the temporal information. We thoroughly evaluate the contributions of each component in our system and show that it achieves state-of-the-art performance on the FERA-2015 Challenge dataset.


Jaiswal, S., & Valstar, M. F. (2016). Deep learning the dynamic appearance and shape of facial action units.

Conference Name Winter Conference on Applications of Computer Vision (WACV)
End Date Mar 9, 2016
Publication Date Jan 1, 2016
Deposit Date Jan 21, 2016
Publicly Available Date Jan 21, 2016
Peer Reviewed Peer Reviewed
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