An improved cross-matching algorithm for fingerprint images from multi-type sensors

This paper proposes a novel cross-matching algorithm for fingerprint images from multi-type fingerprint sensors. Our method can handle the difference of fingerprint images which results by the different characteristics of fingerprint sensors. By using core detection based fingerprint registration and a two-level transformation - image space normalization and feature space normalization, all feature points of fingerprint images are mapped into one feature space. Then the feature points extracted in different sensor images are matched to calculate similarity in the same feature space. Experimental results show that better accuracy can be achieved on crossing matching image dataset after normalization. Our method presents the good potential on image datasets of optical sensors, thermal slice sensors and capacity sensors.