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Emotion Recognition from rPPG via Physiologically Inspired Temporal Encoding and Attention-Based Curriculum Learning.

Changmin Lee¹, Hyunwoo Lee², Mincheol Whang¹

¹Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul 03016, Republic of Korea.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

This study introduces a deep learning framework for non-contact emotion recognition using remote photoplethysmography (rPPG). The model shows promise for arousal detection but highlights limitations for valence recognition, suggesting the need for multimodal approaches.

Keywords:

affective computing autonomic nervous system curriculum learning emotion recognition physiological computing remote photoplethysmography sparse attention temporal dynamics

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Area of Science:

Affective computing
Physiological signal processing
Machine learning for emotion recognition

Background:

Remote photoplethysmography (rPPG) offers non-contact emotion recognition.
Challenges include sparse emotional responses, noise, weak labels, and subtle valence correlates.

Purpose of the Study:

To develop a deep learning framework for robust emotion recognition from rPPG signals.
To address challenges of temporal sparsity, noise, and weak labels in rPPG-based emotion analysis.

Main Methods:

A novel deep learning framework with a Multi-scale Temporal Dynamics Encoder (MTDE).
Adaptive sparse α-Entmax attention and Gated Temporal Pooling for feature aggregation.
A three-phase curriculum learning strategy to manage data complexities.

Main Results:

The temporal-only model achieved competitive arousal recognition accuracy (66.04%) and F1-score (61.97%).
Valence recognition accuracy was lower (62.26%), indicating limitations of unimodal temporal analysis.
Established benchmarks for temporal-only rPPG emotion recognition.

Conclusions:

The proposed framework demonstrates potential for arousal recognition from rPPG.
Unimodal temporal cardiovascular analysis has inherent limitations for valence recognition.
Future research should integrate spatial or multimodal data for comprehensive emotion recognition.