International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 1 (January-February 2026) Submit your research before last 3 days of February to publish your research paper in the issue of January-February.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 1
January-February 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Optimizing CNNs for Contactless Palmprint Recognition

Author(s) Sanjay R. Ganorkar, Tanishka J. Mane, Ruturaj R. Patil, Dipesh N. Sonawane, Vishal D. Rothe
Country India
Abstract Palm printing and palm vein recognition are the newer spheres within the already developed biometrics sector. Although many time-honored techniques have been offered and seen successful implementation over the last twenty years, the deep learning methods still lack all-round development in the palmprint and palm vein recognition. This research intends to study further the strength of deep learning on palmiets and palm vein recognition via 2D and 3D. We performed thorough examination of 17 known convolutional neural networks (CNNs) utilizing multiple databases, e.g., one 3D palmprint database, five 2D palmprint database and two palm vein databases. Our trials cover various network architectures, learning rates, and layer configurations, taking into account not only single mode data but also mixed mode data simultaneously. Results prove that CNNs of classic format show good recognition capabilities, and recent models with improved accuracy show even better achievements. One of the classic CNNs that stands out is Efficient Net. If the recognition accuracy is evaluated, this is the top performer. On the other hand, even though the classic forms of CNNs are fairly good in recognizing various types of tumors, their accuracy is still lower than the traditional methods.
“Abstract” is a necessary section in a research paper. It may be constructed by gathering main points (summary) from each section of the research paper.
Keywords Performance evaluation, convolutional neural network (CNN), biometrics, palmprint, palm vein, deep learning
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-21
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.17585
Short DOI https://doi.org/gtrswj
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 6Cover Page Vol 7 Isu 5Cover Page Vol 7 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com