Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system)

Bekti Maryuni Susanto, Surateno Surateno, Ery Setiyawan Jullev Atmadji, Ardian Hilmi Pramulintang, Galuh Apriliano, Tanti Wulansari, Mukhamad Angga Gumilang

Abstract


Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail.

Keywords


CNN algorithm; Image classification; People characteristic analysis; Student attedance; YoloV5

Full Text:

PDF


DOI: http://doi.org/10.11591/ijict.v13i2.pp272-284

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The International Journal of Informatics and Communication Technology (IJ-ICT)
p-ISSN 2252-8776, e-ISSN 2722-2616
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

Web Analytics View IJICT Stats