TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition

TL;DR

This paper introduces TKFNet, a novel facial expression recognition framework that emphasizes localized texture cues, such as skin micro-changes around key facial regions, to improve recognition accuracy in challenging real-world conditions.

Contribution

The paper proposes a new FER architecture that explicitly models Texture Key Driver Factors using a Texture-Aware Feature Extractor and Dual Contextual Information Filtering, enhancing discriminative feature extraction.

Findings

Achieves state-of-the-art results on RAF-DB and KDEF datasets.

Effectively captures subtle texture cues for improved FER accuracy.

Demonstrates robustness across diverse facial expressions.

Abstract

Facial expression recognition (FER) in the wild remains a challenging task due to the subtle and localized nature of expression-related features, as well as the complex variations in facial appearance. In this paper, we introduce a novel framework that explicitly focuses on Texture Key Driver Factors (TKDF), localized texture regions that exhibit strong discriminative power across emotional categories. By carefully observing facial image patterns, we identify that certain texture cues, such as micro-changes in skin around the brows, eyes, and mouth, serve as primary indicators of emotional dynamics. To effectively capture and leverage these cues, we propose a FER architecture comprising a Texture-Aware Feature Extractor (TAFE) and Dual Contextual Information Filtering (DCIF). TAFE employs a ResNet-based backbone enhanced with multi-branch attention to extract fine-grained texture representations, while DCIF refines these features by filtering context through adaptive pooling and attention mechanisms. Experimental results on RAF-DB and KDEF datasets demonstrate that our method achieves state-of-the-art performance, verifying the effectiveness and robustness of incorporating TKDFs into FER pipelines.