Measuring physiological signals from the human face and body using cameras is an emerging topic that has grown rapidly in the last decade (2007-2017). It has been shown that supervised and unsupervised machine learning methods can be used to extract human vital signs (e.g., heart rate, breathing rate, blood oxygenation, pulse transit time, heart rate variability) from everyday images and videos. This has led to various camera-based applications that can directly improve people’s quality of life (e.g., tele-health monitoring, neonatal care and fitness tracking). Imaging methods for recovering vital-signs present new possibilities for computer vision applications that require deep understanding of human behavior, such as affective computing. However, the lack of sessions/workshops on the topic of non-contact physiological measurement in top computer vision conferences is notable and has limited the development of methods that leverage some of the latest advances in computer vision and machine learning (e.g., deep learning, adversarial networks, etc.).
The First International Workshop on Computer Vision for Physiological Measurement (CVPM) aims to unite the researchers working in this field, and those who can directly/indirectly benefit from and/or contribute to it (including computer vision and machine learning researchers, doctors/clinicians, medical experts and psychologists). Although targeted at a computer vision audience, and aimed at promoting advancements in methods, a unique aspect of this workshop is that it brings a rich set of compelling applications (e.g., from emotion recognition to medicine to face anti-spoofing and biometric security) that attracts audiences from fields beyond computer science.