A Multiscale Network with Supervised Contrastive Learning for Real-Time Facial Emotion Recognition

2026-05-31

AI Summary

This study addresses the challenge of real-time facial emotion recognition in videos, where large inter-individual variability and subtle, continuous expression dynamics hinder performance. To tackle this, the authors propose a deep neural network that integrates multi-scale feature extraction with supervised contrastive learning. By explicitly modeling the temporal evolution of facial expressions, the method effectively captures fine-grained emotional distinctions and substantially enhances model generalization. Extensive experiments on multiple benchmark datasets demonstrate that the proposed approach achieves state-of-the-art recognition accuracy while maintaining real-time inference speed, offering robust affective perception capabilities for practical applications such as psychological counseling.

Abstract

Real-time emotion recognition from facial expressions is a challenging task, particularly in video-based scenarios where multiple emotional states may occur over time. The difficulty increases further due to the fact that each emotional state is associated with facial expressions that vary significantly across individuals. The change of facial expressions portraying emotional state is not discrete, but rather continuous, which is very challenging to represent through computational aids. A system with the ability to detect variations in facial expressions can have a significant impact on determining the emotional state of an individual. Such a system can be very beneficial for psychologists during counseling by providing additional insights into the emotional state of a subject. In this paper, a deep learning-based system is presented to detect emotional changes in real-time video of a person by modeling the change in facial expressions. The current study is conducted on a standard dataset for training of the deep learning system and the system has provided very satisfactory outcomes in this respect.

Problem

• real-time facial emotion recognition
• continuous emotional states
• individual variation in facial expressions
• video-based emotion analysis
• facial expression dynamics

Innovation

• Multiscale Network
• Supervised Contrastive Learning
• Real-Time Facial Emotion Recognition
• Continuous Emotional States
• Deep Learning