Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi

Authors

  • Fadhillah Azmi Department of Electrical Engineering, Universitas Medan Area, Medan, Indonesia Author
  • Amir Saleh Department of Informatics Engineering, Universitas Prima Indonesia, Medan, Indonesia Author
  • Achmad Ridwan Department of Informatics Engineering, Universitas Prima Indonesia, Medan, Indonesia Author

DOI:

https://doi.org/10.55524/ijircst.2023.11.1.9

Keywords:

Smart Attendance SysteM, Face Recognition, Raspberry Pi, LBP, Normalized Cross Correlation

Abstract

 In this study, a smart attendance system was  created using computer vision techniques embedded in the  Raspberry Pi device. The initial process is carried out by  recording students taking certain courses and taking facial  images for the needs of the system database. In the next  stage, the system will be regulated according to the time of  lecture entry to determine which students will attend the  lecture. Every student who wants to enter the classroom is  identified by taking facial images with a camera from the  Raspberry Pi device to identify and determine the time  students enter to attend lectures. Each image taken will be  processed to detect the presence of a face using the Viola Jones method and to extract features using the LBP method  to obtain the feature value of each image. The results  obtained will be stored in the system for the facial  recognition process. The final stage of the system being built  is to perform face recognition according to the initial image  to carry out the attendance process. This process will be  carried out using the normalized cross correlation (NCC)  technique, in which the highest feature similarity obtained  between the initial image and the newly captured image is  the result of recognition by the system. From the trials that  have been carried out, the developed system gives good  results in obtaining attendance management in a fairly  efficient manner, and the algorithm proposed for facial  recognition obtains good results with an accuracy rate of  97.54%. 

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Published

2023-01-30

Issue

Vol. 11 No. 1 (2023): International Journal of Innovative Research in Computer Science and Technology (Jan) 2023

Section

Articles

How to Cite

Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi . (2023). International Journal of Innovative Research in Computer Science & Technology, 11(1), 38–44. https://doi.org/10.55524/ijircst.2023.11.1.9
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