An Approach for Automatic Pain Detection through Facial Expression

Automatic pain detection is an emerging area of investigation with convenient applications in health care. The variation in facial expression often provides a clue for occurrence of pain. It provides an important window for the person who cannot verbally describe or rate their level of pain. To meet up the specific necessities, a framework has been designed for extraction of features from the face for automatic pain detection through facial expression. In this framework, Gabor filtering and Principal Component Analysis (PCA) are used as contributive steps that improves the performance of the system in terms of accuracy. To verify the accuracy and robustness of the system, experiments have been conducted on UNBC-McMaster Shoulder Pain Expression Archive Database at both frame level (person dependent) and image level (person independent). The methodology achieves 87.23% accuracy for detection of pain at frame level. Also the methodology achieves 82.43% accuracy for classifying the frames between four pain level (i.e. PSPI of 0, 1, 2 and >=3). The success rate of the methodology for pain detection at image level is 95.5%.