An Efficient Gravitational Search Decision Forest Approach for Fingerprint Recognition

DOI : 10.48129/kjs.20635

Authors

  • Mahesh Kumar Department of Computer Science and Engineering, Baba Mastnath University, Asthal Bohar, Sector-29, Rohtak, India
  • Devender Kumar Department of Computer Science and Engineering, Baba Mastnath University, Asthal Bohar, Sector-29, Rohtak, India

DOI:

https://doi.org/10.48129/kjs.20635

Abstract

Fingerprint based human identification is one of the authentic biometric recognition systems due to the permanence and uniqueness of the finger impressions. There is the extensive usage of fingerprint recognition in personalized electronic devices, security systems, banking, forensic labs, and especially in law enforcement agencies. Although the existing systems can recognize fingerprints, they lack in case of poor quality and latent fingerprints. The latent fingerprints are captured by law enforcement agencies during the crime scene to find the criminal. Consequently, it is essential to develop a novel system that can efficiently recognize both complete and latent fingerprints. The current work proposes an efficient Gravitational Search Decision Forest (GSDF) method, which is a combination of the gravitational search algorithm (GSA) and the random forest (RF) method. In the proposed GSDF approach, the mass agent of GSA determines the solution by constructing decision trees in accordance with the random forest hypothesis. The recognition of the fingerprints is accomplished by mass agents in the form of a final generated decision forest from the set of hypothesis space as the mass agents can create multiple hypotheses using random proportional rules. The experiments for fingerprint recognition are conducted for both the latent fingerprints (NIST SD27 dataset) and the complete fingerprints (FVC2004 dataset). The effectiveness of the proposed GSDF approach is analyzed by evaluating the results with machine learning classifiers (random forest, decision tree, back propagation neural networks, and k-nearest neighbor) as well. The comparative analysis of the proposed approach and incorporated machine learning classifiers indicates the outperformed performance of the proposed approach.   

Published

04-08-2022

Issue

Section

Computer Science