Relative Facial Action Unit Detection
This work addresses the challenge of robust facial expression analysis for applications like human-computer interaction, though it is incremental as it builds on existing AU detection methods.
The paper tackles the problem of subject-independent facial action unit detection without requiring a neutral face reference by introducing a relative detection approach that analyzes temporal changes in expression. The method significantly outperforms conventional absolute techniques on three public datasets.
This paper presents a subject-independent facial action unit (AU) detection method by introducing the concept of relative AU detection, for scenarios where the neutral face is not provided. We propose a new classification objective function which analyzes the temporal neighborhood of the current frame to decide if the expression recently increased, decreased or showed no change. This approach is a significant change from the conventional absolute method which decides about AU classification using the current frame, without an explicit comparison with its neighboring frames. Our proposed method improves robustness to individual differences such as face scale and shape, age-related wrinkles, and transitions among expressions (e.g., lower intensity of expressions). Our experiments on three publicly available datasets (Extended Cohn-Kanade (CK+), Bosphorus, and DISFA databases) show significant improvement of our approach over conventional absolute techniques. Keywords: facial action coding system (FACS); relative facial action unit detection; temporal information;