GDL

Journal from gdlhub / 2022-02-12 15:14:13
Oleh : Dwi Sunaryono, Joko Siswantoro, Radityo Anggoro, King Saud University
Dibuat : 2022-02-12, dengan 0 file

Keyword : Course attendance system, Face recognition, Android based, Smartphone
Url : http://www.sciencedirect.com/science/article/pii/S1319157818309406
Sumber pengambilan dokumen : web

Student attendance system is needed to measure student participation in a course. Several automated attendance systems have been proposed based on biometric recognition, barcode, QR code, and near field communication mobile device. However, the previous systems are inefficient in term of processing time and low in accuracy. This paper aims to propose an Android based course attendance system using face recognition. To ensure the student attend in the course, QR code contained the course information was generated and displayed at the front of classroom. The student only needed to capture his/her face image and displayed QR code using his/her smartphone. The image was then sent to server for attendance process. The experimental result shows that the proposed attendance system achieved face recognition accuracy of 97.29 by using linear discriminant analysis and only needed 0.000096s to recognize a face image in the server.

Deskripsi Alternatif :

Student attendance system is needed to measure student participation in a course. Several automated attendance systems have been proposed based on biometric recognition, barcode, QR code, and near field communication mobile device. However, the previous systems are inefficient in term of processing time and low in accuracy. This paper aims to propose an Android based course attendance system using face recognition. To ensure the student attend in the course, QR code contained the course information was generated and displayed at the front of classroom. The student only needed to capture his/her face image and displayed QR code using his/her smartphone. The image was then sent to server for attendance process. The experimental result shows that the proposed attendance system achieved face recognition accuracy of 97.29 by using linear discriminant analysis and only needed 0.000096s to recognize a face image in the server.

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