Enhancing latent palmprints using frequency domain analysis
Issued Date
2024-09-01
Resource Type
ISSN
26673053
Scopus ID
2-s2.0-85198092771
Journal Title
Intelligent Systems with Applications
Volume
23
Rights Holder(s)
SCOPUS
Bibliographic Citation
Intelligent Systems with Applications Vol.23 (2024)
Suggested Citation
Khodadoust J., Monroy R., Medina-Pérez M.A., Loyola-González O., Areekul V., Kusakunniran W. Enhancing latent palmprints using frequency domain analysis. Intelligent Systems with Applications Vol.23 (2024). doi:10.1016/j.iswa.2024.200414 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/99682
Title
Enhancing latent palmprints using frequency domain analysis
Corresponding Author(s)
Other Contributor(s)
Abstract
Latent palmprints are integral to crime scene investigations, constituting a significant portion of encountered prints. They often suffer from poor ridge impressions, noise, and pronounced creases, setting them apart from other palmprint types. While progressive enhancement techniques are widely used for fingerprints, palmprints with numerous thick creases and larger sizes benefit more from region-growing techniques. Frequency domain-based palmprint enhancement excels in separating creases from ridges and reshaping ridge structures accurately. The key challenge lies in identifying suitable initial blocks for both region-growing and iterative enhancement techniques. Existing frequency domain-based quality maps, primarily designed for fingerprints, exhibit limited performance when applied to palmprints, especially latent ones. To address these issues, this paper introduces a new approach that combines region-growing and frequency domain-based enhancement techniques to improve latent palmprints. Our method leverages high-quality blocks, employs the orientation field obtained in the frequency domain to correct possible orientation errors in starting blocks, and utilizes varying weights to enhance all block types effectively. The experimental results indicate that the proposed approach surpasses the existing state-of-the-art techniques in terms of recognition accuracy.