Pain Detection/Classification Framework including Face Recognition based on the Analysis of Facial Expressions for E-Health Systems

Fatma Elgendy¹, Mahmoud Alshewimy², and Amany Sarhan²

¹Kafrelshiekh Higher Institute for Engineering and Technology, Egypt

²Computer and Control Engineering Department, Tanta University, Egypt

Abstract: Facial expressions can demonstrate the presence and degree of pain of humans, which is a vital topic in E-healthcare domain specially for elderly people or patients with special needs. This paper presents a framework for pain detection, pain classification, and face recognition using feature extraction, feature selection, and classification techniques. Pain intensity is measured by Prkachin and Solomon pain intensity scale. Experimental results showed that the proposed framework is a promising one compared with previously works. It achieves 91% accuracy in pain detection, 99.89% accuracy in face recognition, and 78%, 92%, 88% accuracy, respectively, for three levels of pain classification.

Keywords: E-health, Gabor filter, Adaboost, relieff filter, SADE, KNN.

Received January 12, 2020; accepted March 19, 2020