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Design and Development of a Secure door access module using Finger print, Face and Pin authentication

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Author Affiliations

  • 1Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 2Department of Electronics and Computer Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 3Department of Electronics and Computer Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 4Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 5Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 6Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 7Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 8Department of Physics and Industrial Physics, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • 9Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria (TET Fund Centre of Excellence in Biomedicine, Engineering and Agricultural Translation Studies)
  • 10Department of Physics, University of Abuja, Nigeria

Res. J. Engineering Sci., Volume 15, Issue (2), Pages 6-10, May,26 (2026)

Abstract

This paper presents the design and development of a secure door access module that integrates fingerprint recognition, facial authentication, and PIN verification into a single multi-factor security system. The system addresses the growing need for robust, user-friendly, and reliable access control in smart buildings, restricted facilities, and Internet of Things (IoT) ecosystems. The hardware architecture comprises an Arduino Mega microcontroller, a high-resolution capacitive fingerprint sensor (R305), a Raspberry Pi Camera Module V2 for facial recognition, a 4×4 matrix keypad for PIN entry, a magnetic door lock, a relay module, an LCD display, and a buzzer for audio-visual feedback. The software framework leverages OpenCV and Dlib libraries for face detection and recognition, while fingerprint matching is achieved through minutiae-based algorithms. The system operates in three independent authentication modes, each granting access upon successful verification. Experimental results show that the system achieves a false acceptance rate (FAR) of 0.01% and a false rejection rate (FRR) of 0.1% under controlled conditions. The system also incorporates a privacy-preserving biometric template protection scheme and an adaptive access control model that temporarily locks out users after three consecutive failed attempts. The proposed solution demonstrates strong resilience against common attack vectors such as spoofing and key duplication. This work contributes a practical, low-cost, and scalable biometric access control solution suitable for residential, institutional, and commercial applications.

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