Hierarchical palm-print recognition system for large databases

Abstract

As palm-print recognition systems gain popularity in the market, there is a growing need to design efficient and accurate algorithms. There is a possibility especially in big organizations that large number of people will make use of the system. It is required that the system is robust enough to handle such a large amount of data. The number of users could be of the order of several hundreds or even thousands. In this work, a 3 stage recognition process has been developed which ensures fast operation while maintaining a high level of accuracy and is capable of handling large databases. The two distinct levels of matching are coarse-level matching and fine-level matching. The first 2 stages are coarse level matching stages which make use of hand geometry and standard deviation values of the local intensity levels. The 3rd stage is a fine level matching stage which uses Gabor filtering to generate Palm-print Phase and Orientation Code (PPOC) and matches two feature sets using modified Hamming distance. The results obtained are very impressive and clearly show the advantage of using a 3-stage process.