A multibiometric system based on finger photo and palm photo
Issued Date
2026-02-01
Resource Type
ISSN
13807501
eISSN
15737721
Scopus ID
2-s2.0-105029919978
Journal Title
Multimedia Tools and Applications
Volume
85
Issue
2
Rights Holder(s)
SCOPUS
Bibliographic Citation
Multimedia Tools and Applications Vol.85 No.2 (2026)
Suggested Citation
Khodadoust J., Monroy R., Medina-Pérez M.A., Kusakunniran W., Khodadoust A.M. A multibiometric system based on finger photo and palm photo. Multimedia Tools and Applications Vol.85 No.2 (2026). doi:10.1007/s11042-026-21409-0 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115173
Title
A multibiometric system based on finger photo and palm photo
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Recognition of finger photos and palm photos is an emerging area in biometrics, primarily leveraging textural attributes for authentication. Limited efforts have explored the application of deep neural networks (DNNs) for finger photo minutiae extraction, while palm photo minutiae extraction remains challenging due to issues such as blurriness and creases, which can lead to false minutiae. In this paper, we introduce a generative adversarial network (GAN) that utilizes frequency-domain patches to deblur and enhance image quality, effectively addressing both deblurring and crease removal. Furthermore, we present a deep convolutional neural network (DCNN) for minutiae extraction from enhanced patches, alongside a model for singular point (SP) detection. Additionally, we propose a score-based multibiometric system that seamlessly integrates palm and finger photos without the need for score normalization. To validate the effectiveness of our approach, we conducted extensive experiments on a database comprising 30,000 hand photos from 2,500 volunteers, as well as a smaller dataset of 2,400 hand photos from 200 volunteers for cross-database evaluation. Our results demonstrate the enhanced accuracy of our models and establish the superiority of our multibiometric system over state-of-the-art (SOTA) methods.