Browse > Article
http://dx.doi.org/10.33778/kcsa.2022.22.1.029

Design and development of non-contact locks including face recognition function based on machine learning  

Yeo Hoon Yoon (세종대학교 정보보호학과)
Ki Chang Kim (대전대학교 컴퓨터공학과)
Whi Jin Jo (대전대학교 컴퓨터공학과)
Hongjun Kim (대전대학교 컴퓨터공학과)
Publication Information
Convergence Security Journal / v.22, no.1, 2022 , pp. 29-38 More about this Journal
Abstract
The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.
Keywords
Door Lock System; LBP Algorithm; Haar-based cascade; Face Recognition; Non-contact;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 M. Coskun, A. Ucar, O. Yildirim, and Y. Demir, "Face recognition based on convolutional neural network," proceedings of 2017 International Conference on Modern Electrical and Energy Systems (MEES), Nov. 2017.
2 Soo, Sander, "Object detection using Haar-cascade Classifier," Institute of Computer Science, University of Tartu, pp. 1-12, 2014.
3 J. Choi and J. Kim, "A Study on the road crosswalk detection using Haar feature and Cascade object detector for advanced driver assistance system," proceedings of Korea Information and Communications Society Conference, pp. 840-840, 2018.
4 Gudipati, Vamshi Krishna, et al,."Efficient facial expression recognition using adaboost and haar cascade classifiers." proceedings of 2016 Annual Connecticut Conference on Industrial Electronics, Technology & Automation, 2016.
5 Hamid Cheraghi, Hyo Jong Lee, "Face recognition using local binary patterns and K-Nearest Neighbors," proceedings of the Korean Institute of Information Scientists and Engineers, pp. 1424-1426, 2018.
6 W.K. Jung, Y.T.Cho, Y.H. Ahn, and O.S. Chae, "A study on local micro pattern for facial expression recognition," Journal of Convergence Security, vol. 14, no. 5, Sep. 2014.
7 Padilla, Rafael, C. F. F. Costa Filho, and M. G. F. Costa, "Evaluation of haar cascade classifiers designed for face detection," World Academy of Science, Engineering and Technology, vol. 64, pp. 362-365, 2012.
8 Setjo, Christian Herdianto, and Balza Achmad, "Thermal image human detection using Haar-cascade classifier," proceedings of 2017 7th International Annual Engineering Seminar (InAES), 2017.