Implementation of Facial Recognition for Home Security Systems

  • Authors

    • Arnab Pushilal
    • Sulakshana Chakraborty
    • Raunak Singhania
    • P. Mahalakshmi
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20706
  • Arduino Uno, Covariance, Eigenvalue, Eigenvector, Eigen face, Eigen vector, Euclidean distance, Manhattan distance, PCA
  • In this paper, the design and development of a home security system has been detailed which uses facial recognition to conform the identity of the visitor and taking various security measures when an unauthorized personnel tries accessing the door. It demonstrates the implementation of one of the most popular algorithm for face recognition i.e. principal component analysis for the purpose of security door access. Since PCA converts the images into a lower dimension without losing on the important features, a huge set of training data can be taken. If the face is recognized as known then the door will open otherwise it will be categorized as unknown and the microcontroller (Arduino Uno) will command the buzzer to start ringing.

     

     

  • References

    1. [1] Automatic Door Access System Using Face Recognition Hteik Htar Lwin, Aung Soe Khaing, Hla Myo Tun

      [2] . Shemi P M, Ali M A, A Principal Component Analysis Method for Recognition of Human Faces: Eigenfaces Approach, International Journal of Electronics Communication and Computer Technology (IJECCT),Volume 2 Issue 3 (May 2012)

      [3] M. Turk, A. Pentland: Face Recognition using Eigenfaces, Conference on Computer Vision and Pattern Recognition, 3 – 6 June 1991, Maui, HI , USA, pp. 586 – 591

      [4] Ayushi Gupta, Ekta Sharma, NehaSachan and Neha Tiwari. Door Lock System through Face Recognition Using MATLAB. International Journal of Scientific Research in Computer Science and Engineering, Vol1, Issue-3, 30 June 2013.

      [5] Survey Paper on Door Level Security using Face Recognition Harshada B. More1 , Anjali R. Bodkhe2 Department of Comp Science & Engg, Government College of Engg, Jalgaon Maharashtra, India1.

      [6] Study of Euclidean and Manhattan Distance Metrics using Simple K-Means Clustering Deepak Sinwar #1 , Rahul Kaushik *2 #Assistant Professor, *M.Tech Scholar Department of Computer Science & Engineering BRCM College of Engineering & Technology, Bahal.

      [7] MohammaAmanullah “MICROCONTROLLER BASED REPROGRAMMABLE DIGITAL DOOR LOCK SECURITY SYSTEM BY USING KEYPAD & GSM/CDMA TECHNOLOGYâ€, IOSR Journal of Electrical and Electronics Engineering (IOSR - JEEE), Volume 4, Issue 6 (Mar. - Apr. 2013).

      [8] Principal component analysis Herve Abdi and Lynne J. Williams2J. H. Davis and J. R. Cogdell

      [9] ] K. I. Diamantaras and S. Y. Kung, “Principal Component Neural Networks: Theory and Applicationsâ€, John Wiley & Sons,Inc., 1996

      [10] Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA

      [11] Vadivel, A.K.Majumdar & S. Sural, " Performance comparison of distance metrics in content based Image retrieval applications", Intl. Conference on Information Technology Motorola Semiconductor Data Manual, Motorola Semiconductor Products Inc., Phoenix, AZ, USA, 1989.

      [12] P. Jamieson,"Arduino for Teaching Embedded Systems. Are Computer Scientists and Engineering Educators Missing the Boat" International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS'11), 2011

      [13] M. A. Zermani and E. Feki and A. Mami ," Temperature Acquisition and Control System based on the Arduino",International Journal of Emerging Science and Engineering , Vol.2 No.12, pp:1-6,2014.

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  • How to Cite

    Pushilal, A., Chakraborty, S., Singhania, R., & Mahalakshmi, P. (2018). Implementation of Facial Recognition for Home Security Systems. International Journal of Engineering & Technology, 7(4.10), 55-58. https://doi.org/10.14419/ijet.v7i4.10.20706