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Latent fingerprints are important for identifying criminal suspects. However,recognizing a latent fingerprint in a collection of reference fingerprintsremains a challenge. Most, if not all, of existing methods would extractrepresentation features of each fingerprint independently and then compare thesimilarity of these representation features for recognition in a differentprocess. Without the supervision of similarity for the feature extractionprocess, the extracted representation features are hard to optimally reflectthe similarity of the two compared fingerprints which is the base for matchingdecision making. In this paper, we propose a new scheme that can model thepair-relationship of two fingerprints directly as the similarity feature forrecognition. The pair-relationship is modeled by a hybrid deep network whichcan handle the difficulties of random sizes and corrupted areas of latentfingerprints. Experimental results on two databases show that the proposedmethod outperforms the state of the art.

Pair-Relationship Modeling for Latent Fingerprint Recognition