A survey on minutiae-based palmprint feature representations, and a full analysis of palmprint feature representation role in latent identification performance Academic Article in Scopus uri icon

abstract

  • © 2019 The AuthorsLatent palmprint identification is a crucial element for both law enforcement and integrated automated fingerprint identification systems because approximately 30% of the imprints found in a crime scene originate from a human's palms. To find the person whom the palmprint belongs to, forensic experts use systems that automatically compare the imprints found, called latent, against thousands of potential palmprints. Identification systems rely on features obtained from the palmprint, and different feature representations to include discriminative information. However, there is no consensus as to which representation allows for a better matching between latent palmprints, and those with a known identity. Furthermore, evaluating the identification performance when matching palmprints obtained when using different representations has not been done fairly. The current manner of evaluating palmprint identification methods uses different datasets, performance measures, and does not allow to discern the contributions of the feature representation and the methods for matching the palmprints. In this study, we have reviewed those features used for latent palmprint identification, and also we propose an evaluation methodology that allows for a fair comparison of minutiae-based features. Using our methodology, we evaluated each representation performing more than 5 billion comparisons. Our experiments are done using a dataset that includes information about the matching minutiae according to an expert. We aim with our results to provide a baseline for new research in latent palmprint identification feature representations, allowing for a fair comparison of newly developed representations in the future, which would enhance the whole latent palmprint identification methods. For this purpose, we also publicly provide our dataset, methodology implementation, and the feature representations implementation tested in our experiments.

publication date

  • October 1, 2019