The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2024.0150350
PDF

The Application of Improved Scale Invariant Feature Transformation Algorithm in Facial Recognition

Author 1: Yingzi Cong

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 3, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Currently, face recognition models suffer from insufficient accuracy, stability, and computational efficiency. To address this issue, an improved feature extraction algorithm on the ground of Haar wavelet features and scale invariant feature transformation algorithm is proposed. In addition, the study also combines this algorithm with deep belief networks to construct an improved facial recognition model. The effectiveness of the proposed improved feature extraction algorithm was verified, and it was found that the recognition accuracy of the algorithm was 94.2%, which is better than other comparative algorithms. In addition, the study also conducted empirical analysis on the improved facial recognition model and found that the recognition accuracy of the model was 0.92, and the feature matching time was 2.6 seconds, which was better than other comparative models in terms of performance. On the ground of the above results, the proposed facial recognition model has significantly improved recognition accuracy and efficiency compared to traditional models. It can provide theoretical reference for improving the universality of facial recognition applications in different fields.

Keywords: Haar wavelet features; scale invariant feature transformation algorithm; deep belief network; facial recognition; performance improvement

Yingzi Cong, “The Application of Improved Scale Invariant Feature Transformation Algorithm in Facial Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150350

@article{Cong2024,
title = {The Application of Improved Scale Invariant Feature Transformation Algorithm in Facial Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150350},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150350},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Yingzi Cong}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org