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Palmprint Recognition Based on Local Texture Features

In this paper, we propose and evaluate palmprint recognition method based on local Haralick features. The Haralick features are extracted from overlapping square subimages of a palmprint region of interest (ROI). A biometric template is composed of N m-component feature vectors, where N is the total number of overlapping subimages, and m is the number of local Haralick features per subimage in the ROI. A live biometric template and templates from database are matched in N matching modules. Based on fusion at the matchingscore level, the total similarity measures between a live biometric template and templates from the database are calculated. By using the maximum of total similarity measure and the 1-NN classification rule, the final decision (person identity) is made. The proposed palmprint recognition system was tested on the PolyU database. The results of open set identification are given. markan.lopar@fer.hr