Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system) - Maryuni Susanto, Bekti; Surateno, Surateno; Jullev Atmadji, Ery Setiyawan; Pramulintang, Ardian Hilmi; Apriliano, Galuh; Wulansari, Tanti; Angga Gumilang, Mukhamad (International Journal of Informatics and Communication Technology (IJ-ICT), 2024)
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.