Hand-Dorsa Vein Recognition Based on Coded and Weighted Partition Local Binary Patterns

Abstract

In this paper, a new feature descriptor is presented and proposed for personal verification based on near infrared images of hand-dorsa veins. This new feature descriptor is a modification of the previously proposed partition local binary patterns (PLBP) by adding feature weighting and error correction coding (ECC). While addition of feature weighting aims to reduce the influence of insignificant local binary patterns, addition of ECC aims to increase the distances between feature classes by utilizing the systematic redundancy that has been widely used to achieve reliable data transmission in noisy channels. Using a large database with more than two thousand hand-dorsa vein images, the resulting new feature descriptor, named Coded and Weighted PLBP (WCPLBP), is shown to be more effective than the original PLBP without feature weighting and ECC, and offers a better performance in recognition of hand-dorsa vein images with a correct recognition rate reaching approximately 99% using a simple nearest neighbor classifier.