Palmprint identification is still considered as a challenging research line in Biometrics. Nowadays, the performance of this techniques highly depends on the quality of the involved palmprints, specially if the identification is performed in latent palmprints. In this paper, we propose a new feature model for representing palmprints and dealing with the problems of missing and spurious minutiae. Moreover, we propose a novel verification algorithm based in this feature model, which uses a strategy for finding adaptable local matches between substructures obtained from images. In experimentation, we show that our proposal achieves highest scores in latent palmprint matching, improving some of the best results reported in the literature.
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