Bimodal recognition based on thumbprint and thumb image using bayesian classfier

Abstract

The purpose of this project is to develop a Thumb image classification module which able to predict the gender from the image input. This module can be integrated into the current thumb print recognition system to form a bi-modal biometric system. With this add in module, the performance of the recognition system will significantly increase since the database search time is reduce into almost half when only the gender matched is considered. The development of this module is based on the Bayesian Classifier method by having input of the textural analysis, thumb area consumption and the thumb width size. The textural analysis is using the GLCM (Gray Level Co-occurrence Matrix) with its properties of contrast, correlation, energy and homogeneity. The thumb area and size calculation is based on a cropped image which has the thumb over a certain boundary. Due to the usage model of searching the database, the training set and the verification set is coming from the same data sets. The Bayesian Classifier algorithm is implemented in the MATLAB code. Few GLCM pixel distance analysis was done to evaluate the module performance. With the distance pixel of 2, it had shown the best accuracy among the result of other pixel combination. Result for male matching 82.35% and female matching is 81.82%.