Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape
Since fingerprints are one of the most widely deployed biometrics, accurate fingerprint gender estimation can positively affect several applications. For example, in criminal investigations, gender classification may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Due to its huge variability in size and shape, gender estimation on partial fingerprints is a challenging problem. Therefore, in this work we propose a flexible gender estimation scheme by building a gender classifier based on an ensemble of minutiae. The outputs of the single minutia gender predictions are combined by a novel adjusted score fusion approach to obtain an enhanced gender decision. Unlike classical solutions this allows to deal with unconstrained fingerprint parts of arbitrary size and shape. We performed investigations on a publicly available database and our proposed solution proved to significantly outperform state-of-the-art approaches on both full and partial fingerprints. The experiments indicate a reduction in the gender estimation error by 19.34% on full fingerprints and 28.33% on partial captures in comparison to previous work.