 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: December 2025
Volume 10 | Issue 12
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19445
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 144
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Multimodal Biometrics for Human Identification using RCNN Method |
| Authors Name: | Boda Aruna , M. Kezia Joseph |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_200152
|
| Published Paper Id: | IJRTI2501016 |
| Published In: | Volume 10 Issue 1, January-2025 |
| DOI: | |
| Abstract: | Multimodal biometrics using Region-based Convolutional Neural Networks (RCNN) is an advanced approach for human identification that leverages multiple biometric traits, such as face, fingerprint, iris, or voice, to improve accuracy and robustness. Here's an outline of the concept: Introduction to Multimodal Biometrics: Definition: Combines multiple biometric modalities to enhance recognition accuracy and reliability. Advantages: Improved resistance to spoofing or fraudulent attacks. Higher accuracy by leveraging complementary information from multiple sources. Robustness to missing or low-quality biometric data. Role of RCNN in Biometrics RCNN Overview: A type of neural network that excels in object detection by proposing regions of interest (ROIs) and classifying them. Suitable for localizing and identifying features in biometric data, such as facial landmarks, iris patterns, or fingerprint minutiae. Why RCNN for Biometrics: Precision in identifying key regions in complex biometric inputs. Ability to handle variability in pose, lighting, and occlusions. System Architecture Input Data: Multimodal inputs (e.g., face image, fingerprint scan, and iris scan). Preprocessing for normalization and noise reduction. Feature Extraction: RCNN detects and extracts features from each biometric modality. Region Proposal Network (RPN) identifies regions of interest in the data. Feature Fusion: Combines features from different modalities using techniques like concatenation, weighted averaging, or attention mechanisms. Ensures complementary information is utilized for robust recognition. Classification Fully connected layers classify the combined features to identify individuals. The output includes identity and confidence scores. Challenges and Solutions Challenges: Computational cost of processing multimodal data. Data alignment and synchronization for different modalities. Handling missing or incomplete data. Solutions: Optimize RCNN architecture to reduce complexity. Use imputation techniques for incomplete modalities. Implement parallel processing and GPU acceleration for efficiency. Applications Security: Access control in secure areas, surveillance systems. Healthcare: Patient identification in medical systems. Banking: Authentication for financial transactions. |
| Keywords: | Region-based Convolutional Neural Networks, Biometrics, Fingerprint |
| Cite Article: | "Multimodal Biometrics for Human Identification using RCNN Method", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 1, page no.a86-a89, January-2025, Available :http://www.ijrti.org/papers/IJRTI2501016.pdf |
| Downloads: | 000394 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2501016
Registration ID:200152
Published In: Volume 10 Issue 1, January-2025
DOI (Digital Object Identifier):
Page No: a86-a89 Country: Hyderabad, Telangana, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2501016 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2501016 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2025 - All Rights Reserved - IJRTI | |||