Subject Identification Across Large Expression Variations Using 3D Facial Landmarks
This addresses the problem of identifying individuals despite emotional expression changes, which is incremental as it applies existing methods to new data with specific improvements.
The paper tackles subject identification across large expression variations using 3D facial landmarks, showing that their method outperforms current state-of-the-art on BU-4DFE and BP4D databases and establishes a baseline on BP4D+.
Landmark localization is an important first step towards geometric based vision research including subject identification. Considering this, we propose to use 3D facial landmarks for the task of subject identification, over a range of expressed emotion. Landmarks are detected, using a Temporal Deformable Shape Model and used to train a Support Vector Machine (SVM), Random Forest (RF), and Long Short-term Memory (LSTM) neural network for subject identification. As we are interested in subject identification with large variations in expression, we conducted experiments on 3 emotion-based databases, namely the BU-4DFE, BP4D, and BP4D+ 3D/4D face databases. We show that our proposed method outperforms current state of the art methods for subject identification on BU-4DFE and BP4D. To the best of our knowledge, this is the first work to investigate subject identification on the BP4D+, resulting in a baseline for the community.