Clustering large database of images using multilevel clustering approach for optimized face recognition process

Abstract

In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.