No abstract

No abstract

No abstract

The identification of fingerprints and palmprints is considered a challenging research line in Biometrics. Nowadays, the accuracy of these techniques highly depends on the quality of the involved impressions, specially if the matching is performed in latent cases. In the present work, a new algorithm that reports a high accuracy in both cases is presented. This proposal requires a minimum amount of manually marked information in latent impressions and deals successfully with problems of missing and spurious minutiae. Moreover, we improved a verification algorithm based on a previously introduced feature model. The algorithm uses a strategy for finding adaptable local matches between substructures obtained from images. The experimental results show that our proposal achieves high accuracy for all cases, despite the major differences that exist between a palmprint and a fingerprint. For latent fingerprint identification our approach shows its robustness in retrieving 258 latents from different size scale of the background dataset, achieving a rank-1 identification rate over 57% in all cases. Carrying out a similar experimentation for latent palmprint identification, our approach achieved a rank-1 identification rate over 75%.

This work introduces a new feature based on relative minutia position regarding a reference point. The introduction of this feature, allows the elimination of false matches generated by minutiae. Moreover, a novel algorithm for detecting the reference point in fingerprints is introduced. This approach was tested in a manually edited dataset and it proved to be highly tolerant to distorted impressions. Moreover, the new feature was integrated to a recent fingerprint indexing algorithm in an efficient way. Well known fingerprint datasets were employed to show the improvement in accuracy and the superiority of the presented method regarding other proposals.