Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data

• URL: http://arxiv.org/abs/2409.06154v2
• Date: Fri, 10 Jan 2025 09:44:43 GMT
• Title: Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data
• Authors: Yin Chen, Jia Li, Yu Zhang, Zhenzhen Hu, Shiguang Shan, Meng Wang, Richang Hong,
• Abstract summary: We propose a unified dual-modal learning framework that integrates SFER data as a complementary resource for DFER.<n>S4D employs dual-modal self-supervised pre-training on facial images and videos using a shared Transformer (ViT) encoder-decoder architecture.<n>Experiments demonstrate that S4D achieves a deeper understanding of DFER, setting new state-of-the-art performance.
• Score: 83.48170683672427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic facial expression recognition (DFER) infers emotions from the temporal evolution of expressions, unlike static facial expression recognition (SFER), which relies solely on a single snapshot. This temporal analysis provides richer information and promises greater recognition capability. However, current DFER methods often exhibit unsatisfied performance largely due to fewer training samples compared to SFER. Given the inherent correlation between static and dynamic expressions, we hypothesize that leveraging the abundant SFER data can enhance DFER. To this end, we propose Static-for-Dynamic (S4D), a unified dual-modal learning framework that integrates SFER data as a complementary resource for DFER. Specifically, S4D employs dual-modal self-supervised pre-training on facial images and videos using a shared Vision Transformer (ViT) encoder-decoder architecture, yielding improved spatiotemporal representations. The pre-trained encoder is then fine-tuned on static and dynamic expression datasets in a multi-task learning setup to facilitate emotional information interaction. Unfortunately, vanilla multi-task learning in our study results in negative transfer. To address this, we propose an innovative Mixture of Adapter Experts (MoAE) module that facilitates task-specific knowledge acquisition while effectively extracting shared knowledge from both static and dynamic expression data. Extensive experiments demonstrate that S4D achieves a deeper understanding of DFER, setting new state-of-the-art performance on FERV39K, MAFW, and DFEW benchmarks, with weighted average recall (WAR) of 53.65\%, 58.44\%, and 76.68\%, respectively. Additionally, a systematic correlation analysis between SFER and DFER tasks is presented, which further elucidates the potential benefits of leveraging SFER.

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