Saliency-Based Robust Correlation for Real-Time Face Registration and Verification

We propose a novel person verification system for real-time face identification. The main features of the system include accurate registration of face images using a robust form of correlation, a framework for global registration of a face database using a minimum spanning tree algorithm and a method for selecting a subset of features optimal for discrimination between clients and impostors. The results indicate that the image registration is of high accuracy and the feature selection is successfully improving on the verification performance.