Partial fingerprint identification through checkerboard sampling method using ANN

This paper presents a straightforward method of fingerprint identification considering partial fingerprints through neural network system. The fingerprint templates constitute the largest data in the biometric field. By considering partial fingerprint samples, the amount of data in the database shall be decreased and this will consequently lead to a faster processing for fingerprint identification. To address the problem on database capacity, sampling the full fingerprint is done through checkerboard algorithm. A standard 8×8 checkerboard is adopted wherein selected blocks are considered as samples for Artificial Neural Network (ANN) training. This will reduce the memory size of a fingerprint template to 50%. Also, 25% of the whole fingerprint is done to further reduce the memory size. The fingerprint images underwent the usual image enhancement by FFT and histogram equalization. Then, image binarization is done to be able to use pattern identification tool in an artificial neural network system available in MATLAB.