# CAST-Phys: Contactless Affective States Through Physiological signals Database

arXiv: https://arxiv.org/abs/2507.06080 · HTML: https://mlsanity.com/paper/2507.06080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 85 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement](https://mlsanity.com/paper/1907.11921/) (2019)
- [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021)
- [The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video](https://mlsanity.com/paper/2111.09748/) (2021)
- [PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography](https://mlsanity.com/paper/2006.02699/) (2020)
- [Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner](https://mlsanity.com/paper/2007.06786/) (2020)
- [Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2208.04378/) (2022)
- [PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer](https://mlsanity.com/paper/2302.03548/) (2023)
- [rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement](https://mlsanity.com/paper/2306.02301/) (2023)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [Neuron Structure Modeling for Generalizable Remote Physiological Measurement](https://mlsanity.com/paper/2303.05955/) (2023)
- [Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing](https://mlsanity.com/paper/1905.07039/) (2019)
- [The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation](https://mlsanity.com/paper/2109.10471/) (2021)
- [Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss](https://mlsanity.com/paper/2203.10882/) (2022)
- [Virtual Reality for Emotion Elicitation -- A Review](https://mlsanity.com/paper/2111.04461/) (2021)
- [Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement](https://mlsanity.com/paper/2303.12059/) (2023)
- [SympCam: Remote Optical Measurement of Sympathetic Arousal](https://mlsanity.com/paper/2410.20552/) (2024)
- [End-to-end facial and physiological model for Affective Computing and applications](https://mlsanity.com/paper/1912.04711/) (2019)
- [PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows](https://mlsanity.com/paper/2407.21519/) (2024)
- [Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera](https://mlsanity.com/paper/1701.05248/) (2017)

## Abstract (arXiv)

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.