A Secure Bank Locker Access System using Facial Recognition with Liveness- Based Spoofing Detection
Year: 2025
Type: article
Abstract: ABSTRACT — Traditional security mechanisms for bank lockers, such as physical keys and PIN codes, are increasingly vulnerable to theft and unauthorized access. This paper presents the design and implementation of a robust, modern security system that leverages biometric technology to address these vulnerabilities. The proposed system utilizes facial recognition for user authentication, enhanced with a critical liveness detection module to prevent sophisticated spoofing attacks. The system architecture is built on a foundation of computer vision and deep learning techniques, implemented using Python and the OpenCV library. The core functionality involves capturing a user's live facial image, verifying their identity against a pre-enrolled database, and simultaneously performing a liveness check to ensure the subject is a real person and not a static photo or video. The liveness detection model, a Convolutional Neural Network (CNN) named LivenessNet, is specifically trained to distinguish between real and spoofed facial images by analyzing subtle micro-textures. The entire system is integrated into a user-friendly application with a clear workflow for user login, authentication, and access control. This paper details the system's functional and non-functional requirements, architectural design using UML diagrams, and the implementation of the core algorithms. The result is a highly secure, convenient, and reliable alternative to conventional locker security systems, setting a new benchmark for protecting valuable assets in the banking sector. Keywords— Facial Recognition, Liveness Detection, Biometric Security, Anti-Spoofing, Bank Locker System, Convolutional Neural Network (CNN), Computer Vision.
DOI: 10.55041/ijsrem52083
Authors: Manjunath B B, Shruthi M T
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4413618899