Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning

Abstract

Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, are automatically detected. The temperature of each of these regions is then extracted and used as input to a classifier, specifically a Support Vector Machine, which outputs three states: baseline, stressed, and relaxed. The proposal was developed and tested on thermal images of 25 participants who were subjected to a stress-inducing protocol followed by relaxation techniques. After testing the developed methodology, an accuracy of 95.4% and an error rate of 4.5% were obtained. The methodology proposed in this study allows the automatic classification of a person’s stress state based on a thermal image of the face. This represents an innovative tool applicable to specialists. Furthermore, due to its robustness, it is also suitable for online applications.

Development an Artificial Vision for a Parallel Manipulator Using M2M Technologies

“…The most important factor in the development of intelligent robots is in the criteria for ensuring high reliability and flexibility of the system being developed, as well as in the development of an effective system of adaptation to different circumstances and requirements. In this case, the introduction of artificial intelligence in the robotic system helps developers to increase the productivity of the process by reducing energy consumption for data processing, with a further reduction of time to perform various operations in real-time [7,8].…”

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies

“…The most important factor in the development of intelligent robots lies in ensuring high reliability and flexibility of the system being developed, as well as in creating an effective system of adaptation to different circumstances and requirements. In this case, the introduction of artificial intelligence into the robotic system helps developers increase productivity by reducing energy consumption for data processing and further decreasing the time required to perform various operations in real-time [ 7 , 8 ].…”