Statistical Descriptors-based Automatic Fingerprint Identification: Machine Learning Approaches

TL;DR

This study evaluates machine learning techniques for automatic fingerprint identification using statistical descriptors from low-quality latent fingerprints, demonstrating that Random Forests and J48 classifiers improve identification accuracy.

Contribution

The paper introduces a novel approach using GLCM-based statistical descriptors and compares multiple machine learning algorithms for latent fingerprint identification.

Findings

Random Forests and J48 outperform other classifiers in accuracy.

Statistical descriptors from GLCM effectively characterize fingerprint features.

The approach improves identification accuracy for low-quality latent fingerprints.

Abstract

Identification of a person from fingerprints of good quality has been used by commercial applications and law enforcement agencies for many years, however identification of a person from latent fingerprints is very difficult and challenging. A latent fingerprint is a fingerprint left on a surface by deposits of oils and/or perspiration from the finger. It is not usually visible to the naked eye but may be detected with special techniques such as dusting with fine powder and then lifting the pattern of powder with transparent tape. We have evaluated the quality of machine learning techniques that has been implemented in automatic fingerprint identification. In this paper, we use fingerprints of low quality from database DB1 of Fingerprint Verification Competition (FVC 2002) to conduct our experiments. Fingerprints are processed to find its core point using Poincare index and carry out enhancement using Diffusion coherence filter whose performance is known to be good in the high curvature regions of fingerprints. Grey-level Co-Occurrence Matrix (GLCM) based seven statistical descriptors with four different inter pixel distances are then extracted as features and put forward to train and test REPTree, RandomTree, J48, Decision Stump and Random Forest Machine Learning techniques for personal identification. Experiments are conducted on 80 instances and 28 attributes. Our experiments proved that Random Forests and J48 give good results for latent fingerprints as compared to other machine learning techniques and can help improve the identification accuracy.