Decision Fusion of Multisensor Images for Human Face Identification in Information Security

Abstract

The chapter is mainly focused on theoretical discussions and experimental observations on decision fusion along with feature level multisensor fusion technique for human face identification especially useful in information security system in order to obtain better recognition rate. Feature level multisensor fusion of optical and infrared images is performed to resolve the difficulties of individual interpretation of visual and infrared images as a first step of the face identification system. ROC curve analysis is also reported to verify the recognition accuracy after classification of fused images using Support Vector Machine (SVM). The authors have performed experiments on IRIS face database in three different groups: full dataset, and two subsets with variation in expression and illumination. Classification accuracy was obtained in two subsets and full dataset as 95%, 94.12%, and 99.08%, respectively after decision fusion.