CAST-Phys: Contactless Affective States Through Physiological signals Database

TL;DR

This paper introduces CAST-Phys, a comprehensive contactless physiological and facial video dataset for emotion recognition, addressing the need for non-intrusive, multi-modal emotion analysis in realistic scenarios.

Contribution

The creation of CAST-Phys, a novel high-quality multi-modal dataset for remote emotion recognition using physiological signals and facial videos, filling a critical gap in affective computing research.

Findings

Physiological signals improve emotion recognition accuracy.

Multi-modal fusion enhances recognition performance.

Remote signals can be effectively recovered from facial videos.

Abstract

In recent years, affective computing and its applications have become a fast-growing research topic. Despite significant advancements, the lack of affective multi-modal datasets remains a major bottleneck in developing accurate emotion recognition systems. Furthermore, the use of contact-based devices during emotion elicitation often unintentionally influences the emotional experience, reducing or altering the genuine spontaneous emotional response. This limitation highlights the need for methods capable of extracting affective cues from multiple modalities without physical contact, such as remote physiological emotion recognition. To address this, we present the Contactless Affective States Through Physiological Signals Database (CAST-Phys), a novel high-quality dataset explicitly designed for multi-modal remote physiological emotion recognition using facial and physiological cues. The dataset includes diverse physiological signals, such as photoplethysmography (PPG), electrodermal activity (EDA), and respiration rate (RR), alongside high-resolution uncompressed facial video recordings, enabling the potential for remote signal recovery. Our analysis highlights the crucial role of physiological signals in realistic scenarios where facial expressions alone may not provide sufficient emotional information. Furthermore, we demonstrate the potential of remote multi-modal emotion recognition by evaluating the impact of individual and fused modalities, showcasing its effectiveness in advancing contactless emotion recognition technologies.