A computational model of the infant pain impressions with Gaussian and Nearest Mean Classifier

In the last recent years, non-invasive methods through image analysis of facial have been proved to be excellent and reliable tool to diagnose of pain recognition. This paper proposes a new feature vector based Local Binary Pattern (LBP) for the pain detection. Different sampling point and radius weighted are proposed to distinguishing performance of the proposed features. In this work, Infant COPE database is used with illumination added. Multi Scale Retinex (MSR) is applied to remove the shadow. Two different supervised classifiers such as Gaussian and Nearest Mean Classifier are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 90% for Infant COPE database.