A Multiple Kernel Learning framework for detecting altered fingerprints

The accurate performance achieved by current bio-metric recognition systems based on automated fingerprints analysis has induced criminals to evade system identification by altering their fingerprints on purpose. In this paper, we propose a novel approach for detecting altered fingerprints. Our method is based on the combination of multiple complementary features, such as minutiae density maps and orientation entropy features, describing the discontinuity of the orientation field at multiple scales. Differently from previous works, we propose to learn the correct weights of different features by adopting a Multiple Kernel Learning framework to enhance the discriminative power of an SVM classifier. Experimental results demonstrate that the proposed approach achieves competitive performance with state-of-the-arts methods.