A MULTIMODAL APPROACH FOR AUTOMATIC DETECTION OF INFANT PAIN USING FACIAL EXPRESSION AND CRYING

Abstract

research on automatic detection of facial expressions has been making great strides over the past decade. One challenge in this area has been in not just recognizing the regular facial expressions but in paying attention to the micro expressions or compound expressions i.e. combining multiple expressions which may be critical to the success of an automated system. For instance, detection of pain/no-pain would be of great benefit especially in case of infant pain recognition. This is because the current standard for assessing neonatal pain is discontinuous and inconsistent as it depends highly on the observer’s bias. And sometimes, the drawbacks of manual pain assessment can result in delayed intervention and inconsistent treatment of pain. Hence, there is a need for machine-based techniques to provide consistent and continuous automatic assessment. Preprocessing, pain analysis or feature extraction, and pain recognition are the three main phases in automatic pain detection. Pain recognition from facial gestures is a specific task within the broader task of facial expression recognition i.e. Pain is a compound expression derived from many facial gestures. Hence, pain detection is a very important task from the point of view of computer vision, since it is a clear step towards automatic detection of spontaneous face expressions. The vast majority of the existing methods in the field of automatic facial expressions recognition use Facial Action Coding System (FACS) to detect facial expressions of only adults. However, literature shows that there is ample scope for research in FACS-based method that is designed specifically to detect infants’ facial expression of pain. Furthermore, the challenge in Facial Expression Recognition of Infant pain is because of the fact that the infants’ facial expressions include additional movements and units that are readily not available in the FACS. The purpose of this paper is to conduct a literature survey on the existing research in the area of automated pain analysis and recognition and facilitate in the development of a multi modal pain assessment system for children and infants. Further, this paper discusses how machine learning methods have been used in the Automatic detection of Pain for improving the overall performance. Techniques like Convolutional neural networks (CNNs) and Long-Short Term Memory (LSTM) networks have gained much popularity in the last decades due to the wide range of their applications in medical image analysis and emotion recognition.