GMR based pain intensity recognition using imbalanced data handling techniques

The presence of imbalanced data distribution is evident in most real-life datasets. The problem of learning from imbalanced data is a challenging task due to presence of underrepresented data and severe class distribution skews. In this paper we recognizes 15 different levels of shoulder pain intensities based on facial expressions using UNBC-McMaster Shoulder Pain Expression Archive database which has highly imbalanced data distribution among its classes. A 22 dimensional geometric features are extracted from detected facial landmarks. The feature set is balanced using Synthetic Minority Oversampling Technique (SMOTE) and also using Adaptive Synthetic Sampling (ADASYN). A recognition technique is developed using Gaussian Mixture Regression (GMR) to recognize the fifteen different intensity levels. Comprehensive experiments with various settings show that the proposed pain intensity recognition system using SMOTE and GMR yields stable and promising recognition results.