Creating an advanced Face Detection and Recognition algorithm for Enhanced Information security

Authors

  • Dinesh Bhoyar Dept. of ET, Yeshwantrao Chavan College of Engg, Nagpur, INDIA
  • Diksha Ninave Dept. of ET, Yeshwantrao Chavan College of Engg, Nagpur, INDIA
  • Pravin Zode Dept. of EE, Yeshwantrao Chavan College of Engg, Nagpur, INDIA
  • Arvind R. Bhagat Patil Dept. of CT, Yeshwantrao Chavan College of Engg, Nagpur, INDIA
  • Shivkumar Karale Dept. of CT, Yeshwantrao Chavan College of Engg, Nagpur, INDIA
  • Rajesh Khobragade Dept. of Electronics & Comp Sci,Shri Ramdeobaba College of Engg, and Management Nagpur, INDIA

Keywords:

Mathematical Modeling, Ordinary Differential Equations, A duopoly economy, Market share Viola Jones, Face detection, Face Recognition, Python, LBPH

Abstract

Face is crucial in our social interactions, serving as the initial focal point for recognition and emotional interpretation. Due to its non-invasive nature, precision, and speedy outcomes, the use of Live Face Recognition has gained a significant amount of attention in security systems. The process of recognizing human faces in real time consists of two steps: Face detection and Face recognition. The reason behind our utilization of Viola-Jones algorithm in face detected is due to its exceptional accuracy and efficient real-time processing capabilities. Additionally, this algorithm is available in Open-CV and can be implemented using Python. When considering Face recognition, there are two key stages to keep in mind - the training phase, the evaluation phase. During the schooling phase, the algorithm is instructed using image sample that needs to be learned. On the other hand, during the estimation phase and the test image is matched against all the previously trained samples in this dataset. Local Binary Patterns Histogram is utilized to extract the facial functions from the detected faces in a stay stream. We can achieve face recognition by utilizing the LBPH method.

Downloads

Download data is not yet available.

References

E. I. Abbas and M. E. Safi, "Face Recognition Rate Using Different Classifier Methods Based on PCA," in Proceedings of the International Conference on Current Research in Computer Science and Engineering (ICCIT), 2017, pp. 37–40.

P. Dhoke and M.P. Parsai, "A MATLAB based Face Recognition using PCA with Back Propagation Neural Network," published in 2014.

N.H. Barnouti, S.S.M. Al-Dabbagh, W.E. Matti, and M.A.S. Naser, "Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance," in International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016.

N. S. Devi and K. Hemachandran, "Face Recognition Using Principal Component Analysis," in International Journal of Computer Science and Information Technologies, Vol. 5 (5), 2014, pp. 6491-6496.

Nisha and M. Dutta, "Improved Face Recognition Using LBP and SVM Optimized by PSO Technique," in International Journal of Engineering Development and Research, Volume 5, Issue 4, 2017, pp. 297–303.

M. Jones and P. Viola, "Rapid object Detection utilizing a Boosted cascade of simple characteristics," in Proceedings of the Conference on Computer Vision and Pattern Recognition, 2001.

P. Singh and A. Sharma, "Face Recognition Using Principal Component Analysis in MATLAB," in International Journal of Scientific Research in Computer Science and Engineering, Volume 3(1), February 2015, pp. 1–5.

A. Ali, S. Haroon, F. Hussain, and M.F. Khan, "Face Recognition using Local Binary Patterns," in Bahria University Journal of Information Communication Technology, 2012, pp. 46–50.

L. I. Kambi, "Enhancing Face Identification with Local Binary Patterns and K nearest Neighbors," in Journal of Imaging, 2017.

K. V and Dr. S. Tale, "Implementation Of Preprocessing And Efficient Blood Vessel Segmentation In Retinopathy Fundus Image," in IJRITCC, Volume 3, Issue 6, 2015.

P. Panchakshari and Dr. S. Tale, "Performance Analysis of Fusion technique for EAR Biometrics," in Proceedings of the IEEE Conference on New Trends in Electronics, Information, and Communication, May 2016.

Downloads

Published

05.12.2023

How to Cite

Bhoyar, D. ., Ninave, D. ., Zode, P. ., Arvind R. Bhagat Patil, Karale, S. ., & Khobragade, R. . (2023). Creating an advanced Face Detection and Recognition algorithm for Enhanced Information security. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 584–588. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4175

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)