Abstract :

Facial Recognition Technology Has Become An Essential Component In Modern Security And Surveillance Systems Due To Its Non-intrusive Nature And Potential For Accurate Identification. This Study Presents A Real-time Face Recognition System Capable Of Detecting And Identifying Multiple Faces Simultaneously From A Live Camera Feed. The System Uses Advanced Face Detection And Encoding Techniques To Extract Facial Features And Perform Multi-face Matching Against A Database Of Registered Reference Images. To Improve Reliability And Reduce Errors, A Confidence-based Similarity Threshold Is Implemented, Allowing The System To Differentiate Between High- Probability Matches, Possible Matches, And Non-matches.The System Architecture Is Designed For High-speed Processing, Employing Frame Optimization And Efficient Face Encoding Strategies To Handle Multiple Faces Without Significant Latency. Testing Demonstrates That The System Maintains Robust Performance Under Varying Lighting Conditions, Partial Occlusions, And Dynamic Environments, Making It Suitable For Applications Such As Access Control, Crowd Monitoring, And Security Surveillance.Experimental Evaluation Shows That The Approach Can Effectively Recognize Faces In Complex Scenarios And Provide Timely Alerts, Enhancing Situational Awareness And Security Management. The System Provides A Comprehensive Solution That Combines Automated Detection, Real-time Monitoring, And Threshold-based Alert Mechanisms, Making It An Effective Tool For Safety- Critical Environments.

Published:

10-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

289-297

Section:

Articles

License:

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

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