Abstract
Facial expression spotting, identifying periods where facial expressions occur in a video, is a significant yet challenging task in facial expression analysis. The issues of irrelevant facial movements and the challenge of detecting subtle motions in micro-expressions remain unresolved, hindering accurate expression spotting. In this paper, we propose an efficient framework for facial expression spotting. First, we propose a Compact Sliding-Window-based Multi-temporal-Resolution Optical flow (CSW-MRO) feature, which calculates multi-temporal-resolution optical flow of the input image sequence within compact sliding windows. The window length is tailored to perceive complete micro-expressions and distinguish between general macro- and micro-expressions. CSW-MROcan effectively reveal subtle motions while avoiding the optical flow being dominated by head movements. Second, we propose SpotFormer, a multi-scale spatio-temporal Transformer that simultaneously encodes spatio-temporal relationships of the CSW-MROfeatures for accurate frame-level probability estimation. In SpotFormer, we use the proposed Facial Local Graph Pooling (FLGP) operation and convolutional layers to extract multi-scale spatio-temporal features. We show the validity of the architecture of SpotFormer by comparing it with several model variants. Third, we introduce supervised contrastive learning into SpotFormer to enhance the discriminability between different types of expressions. Extensive experiments on SAMM-LV, CAS(ME)2, and CAS(ME)3 show that our method outperforms state-of-the-art models, particularly in micro-expression spotting. Code is available at https://github.com/KinopioIsAllIn/SpotFormer.
Yicheng Deng
PhD Student
Hideaki Hayashi
Associate Professor
Hideaki Hayashi is an associate professor with D3 Center, the University of Osaka. His research interests focus on neural networks, machine learning, and medical data analysis.
Hajime Nagahara
Professor
He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.