Patch Based Latent Fingerprint Matching Using Deep Learning
Jude Ezeobiejesi, Bir Bhanu · 2018
Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. Such fingerprints are usually incomplete or partial, making it challenging to match them to full fingerprints registered in fingerprint databases. Latent fingerprints may contain few minutiae and no singular structures. Matching algorithms that entirely rely on minutiae or alignment of singular structures fail when those structures are missing. This paper presents an approach for matching latent to rolled fingerprints using the (a) similarity of learned representations of patches and (b) the minutiae on the correlated patches. A deep learning network is used to learn optimized representations of image patches. Similarity scores between patches from the latent and reference fingerprints are determined using a distance metric learned with a convolutional neural network. The matching score is obtained by fusing the patch and minutiae similarity scores. The proposed system was tested by matching fingerprints segmented from the 258 latent fingerprints in the NIST SD27 database against a database of 2,257 rolled fingerprints from NIST SD27 and SD4 databases. Experimental results show a rank-1 identification rate of 81.35% and highlights the promise of our proposed approach.