A hybrid method for fingerprint image quality calculation

This paper proposes a new hybrid scheme to measure fingerprint image quality by combining both local and global features of a fingerprint image. Distinguished from traditional methods (e.g. local standard deviation or orientation information based method, etc.), not only the local texture features but also some global factors such as foreground area, central position of foreground, the number of minutiae and the existence of singular points, are taken into account in the proposed method. Besides the detail definitions of seven quality indices, two weighting methods are also proposed for finding the correlation between the final quality value and each quality index. Experimental results on FVC2002 and our private database show that the EER (equal error rate) value can be downed by 12%-34% with 10% images rejected. It demonstrates that the hybrid method is an effective and efficient scheme to discard poor quality images and, hence, can be used to guarantee the reliability and performance of fingerprint recognition system.