Relative Facial Action Unit Detection

TL;DR

This paper introduces a novel relative AU detection method that analyzes temporal neighborhood to improve robustness and accuracy in facial expression analysis without needing a neutral face reference.

Contribution

It proposes a new classification objective that compares current and neighboring frames, enhancing AU detection robustness against individual differences and expression transitions.

Findings

Significant improvement over absolute methods on CK+, Bosphorus, and DISFA datasets.

Enhanced robustness to face scale, shape, and age-related wrinkles.

Effective in scenarios lacking neutral face references.

Abstract

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;