Two-Level Security System Using Barcode Scanning and Face Recognition for File and Folder Protection with Message Alert

A two-level security system is proposed, integrating barcode authentication and facial recognition to strengthen access control mechanisms. The system employs barcode scanning as the primary verification step, with real-time facial recognition providing a secondary layer of security to guard against unauthorized access. A custom database securely stores user facial data and barcode identifiers for efficient retrieval. The Mesa framework-based simulation showcases the efficacy of the system. The test condition with 90% success rates of authenticating the authorized user, which was calculated as the successful match in simulated authentication attempts. The machine learning algorithms are convolutional neural networks (CNN) for facial recognition and encryption techniques such as AES for protecting the data to make sure the reliability and confidentiality of the system with regard to safeguarding sensitive resources.

Authors

Institutions

Publication Details

Published
2025-04-04
DOI
https://doi.org/10.1109/iccsai64074.2025.11063771
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Two-Level Security System Using Barcode Scanning and Face Recognition for File and Folder Protection with Message Alert

Manju Khanna, Ganesh Kumar, S Tejas, M. Rushya Shrungan
Advanced Steganography and Watermarking Techniques
article

Two-Level Security System Using Barcode Scanning and Face Recognition for File and Folder Protection with Message Alert

Manju Khanna, Ganesh Kumar, S Tejas, M. Rushya Shrungan
article en

Abstract

A two-level security system is proposed, integrating barcode authentication and facial recognition to strengthen access control mechanisms. The system employs barcode scanning as the primary verification step, with real-time facial recognition providing a secondary layer of security to guard against unauthorized access. A custom database securely stores user facial data and barcode identifiers for efficient retrieval. The Mesa framework-based simulation showcases the efficacy of the system. The test condition with 90% success rates of authenticating the authorized user, which was calculated as the successful match in simulated authentication attempts. The machine learning algorithms are convolutional neural networks (CNN) for facial recognition and encryption techniques such as AES for protecting the data to make sure the reliability and confidentiality of the system with regard to safeguarding sensitive resources.

Amrita Vishwa Vidyapeetham (IN)
Openalex Percentile: Top 85%
Advanced Steganography and Watermarking Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.