Two-stage Approach for Palmprint Identification using Hough Transform and Hausdorff Distance

Abstract

This work proposes a line-based Hough transform method, which extracts global features for coarse-level filtering in two-stage palmprint identification system. This is a novel approach to the application of the Hough transform for feature extraction of palmprint. A principal line detection mechanism in transformed space is also proposed based on a flooding process, which is motivated by rainfalling watershed segmentation algorithm. The local neighbourhood information of principal lines is used as a means to measure similarity as well. It works by first extracting consistent and structurally unique local neighbourhood information from inputs or models, and then voting on the optimal matches. It performs speedy interpretation of input images and retrieval of structurally similar models from large database according to the input. The local information extracted from position and orientation of individual line is used for further fine-level identification. Line-based Hausdorff distance (LHD) algorithm is applied for local line matching. Experiments had been conducted on a palmprint database collected by Hong Kong Polytechnic University. Promising results are achieved