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 et al. REF used domain knowledge and facial geometry to pre-select a relevant image region for a particular AU, passing it to a convolutional and bi-directional Long Short-Term Memory (LSTM) neural network.
Deep learning the dynamic appearance and shape of facial action units
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science