Anomaly detection in facial skin temperature using variational autoencoder

• Published in: Artificial life and robotics Vol. 26; no. 1; pp. 122 - 128
• Main Authors: Masaki, Ayaka, Nagumo, Kent, Lamsal, Bikash, Oiwa, Kosuke, Nozawa, Akio
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.02.2021; Springer Nature B.V
• Subjects: Anomalies; Artificial Intelligence; Autonomic nervous system; Biomarkers; Blood flow; Computation by Abstract Devices; Computer Science; Control; Infrared imaging; Mechatronics; Original Article; Physiology; Psychological stress; Robotics; Skin temperature; Temperature; Thermography; Infrared thermography; Deep learning; Variational autoencoder; Facial skin temperature; Thermal face image; Anomaly detection
• ISSN: 1433-5298; 1614-7456
• DOI: 10.1007/s10015-020-00634-2

Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted attention as a remote biomarker. For example, studies have been reported to estimate human emotions, drowsiness, and mental stress on facial skin temperature. However, it is impossible to make a machine that can discriminate all infinite physiological and psychological states. Considering the practicality of skin temperature, a machine that can determine the normal state of facial skin temperature may be sufficient. In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection model was investigated to evaluate the applicability of variational autoencoder (VAE) to facial thermal images.