Landmarks-assisted Collaborative Deep Framework for Automatic 4D Facial Expression Recognition
This work addresses automatic 4D facial expression recognition, which is important for applications like human-computer interaction and emotion analysis, but it appears incremental as it builds on existing methods with a hybrid approach.
The authors tackled 4D facial expression recognition by proposing a landmarks-assisted collaborative deep framework that combines dynamic images from 4D face scans with landmark feature sequences, achieving a classification accuracy of 96.7% on the BU-4DFE database and outperforming state-of-the-art methods.
We propose a novel landmarks-assisted collaborative end-to-end deep framework for automatic 4D FER. Using 4D face scan data, we calculate its various geometrical images, and afterwards use rank pooling to generate their dynamic images encapsulating important facial muscle movements over time. As well, the given 3D landmarks are projected on a 2D plane as binary images and convolutional layers are used to extract sequences of feature vectors for every landmark video. During the training stage, the dynamic images are used to train an end-to-end deep network, while the feature vectors of landmark images are used train a long short-term memory (LSTM) network. The finally improved set of expression predictions are obtained when the dynamic and landmark images collaborate over multi-views using the proposed deep framework. Performance results obtained from extensive experimentation on the widely-adopted BU-4DFE database under globally used settings prove that our proposed collaborative framework outperforms the state-of-the-art 4D FER methods and reach a promising classification accuracy of 96.7% demonstrating its effectiveness.