Latent Fingerprint Identification System Based on a Local Combination of Minutiae Feature Points

Abstract

Latent fingerprints are the fingerprint traces obtained from various surfaces of objects found at the crime scene. Latent fingerprint quality is, therefore, poor and they suffer from non-linear distortions. This is why latent fingerprints contain partial or incomplete minutia information. Developing an automatic fingerprint identification system (AFIS) for latent fingerprints becomes a major challenge because of a small number of minutia features. Moreover, currently developed AFIS for latent fingerprints are not robust against geometric transformations. In this article, based on the existing partial minutiae characteristics, we propose a method for detecting latent fingerprints. To identify an individual with existing nearest neighbor minutia structures, we develop a scale and rotation invariant algorithm called "Ratio of Minutiae Triangles (RMT)". The algorithm utilizes the features which are based on the local minutiae arrangements around a reference minutia. To deal with missing minutiae in latent fingerprints, we consider minutiae clusters based on nearest minutiae neighborhood relation to form hash structures. In the fingerprint retrieval process, these hash structures are used. On the FVC2004 (

A Study on Automatic Latent Fingerprint Identification System

“…Table 1 highlights the important features of proposed latent fingerprint matching algorithms. It can be observed that the state-ofart matching algorithm [40][41][42]45] performs better than other listed algorithms. Table 2 provides the details of the publicly available fingerprint databases.…”

“…Miguel Angel Medina-Pérez et al [40] presented a clustering technique based on minutiae descriptors to improve MCC, M triplets, and nearby minutiae-based descriptors. The "Latent Minutiae Similarity" (LMS), "Clustered Latent Minutiae Pattern" (CLMP), and "Ratio of Minutiae Triangles" (RMT) algorithms, developed by U. U. Deshpande et al [41] , are alignment-free and rotation/scale-invariant. They clustered minutiae structures around a reference minutia and generated minutiae invariant feature vectors to develop discriminative feature vectors needed for fingerprint matching.…”

An Efficient Gravitational Search Decision Forest Approach for Fingerprint Recognition

“…Jindal & Singla (2021) used an ant colony optimization algorithm for matching the minutiae of latent fingerprints with original fingerprints. Deshpande et al (2021) presented a ratio to minutiae triangles based method which is a rotation and scale invariant approach. The presented method was used for the identification of latent fingerprints.…”