Classification of Micro-Expressions Based on Optical Flow Considering Gender Differences
Abstract
Classification of Micro-Expressions Based on Optical Flow Considering Gender Differences
Incoming article date: 01.02.2025This study presents a method for recognizing and classifying micro-expressions using optical flow analysis and the YOLOv11 architecture. Unlike previous binary detection approaches, this research enables multi-class classification while considering gender differences, as facial expressions may vary between males and females. A novel optical flow algorithm and a discretization technique improve classification stability, while the Micro ROC-AUC metric addresses class imbalance. Experimental results show that the proposed method achieves competitive accuracy, with gender-specific models further enhancing performance. Future work will explore ethnic variations and advanced learning strategies for improved recognition.
Keywords: microexpressions, pattern recognition, optical flow, YOLOv11