CAST-Phys: Contactless Affective States Through Physiological signals Database

• URL: http://arxiv.org/abs/2507.06080v1
• Date: Tue, 08 Jul 2025 15:20:24 GMT
• Title: CAST-Phys: Contactless Affective States Through Physiological signals Database
• Authors: Joaquim Comas, Alexander Joel Vera, Xavier Vives, Eleonora De Filippi, Alexandre Pereda, Federico Sukno,
• Abstract summary: The lack of affective multi-modal datasets remains a major bottleneck in developing accurate emotion recognition systems.<n>We present the Contactless Affective States Through Physiological Signals Database (CAST-Phys), a novel high-quality dataset capable of remote physiological emotion recognition.<n>Our analysis highlights the crucial role of physiological signals in realistic scenarios where facial expressions alone may not provide sufficient emotional information.
• Score: 74.28082880875368
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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