Yüz Tanıma ile Hızlı Yoklama Alma

Year 2022, Issue: 36, 78 - 86, 31.05.2022

Hakan Temiz

https://doi.org/10.31590/ejosat.1100885

Cited By: 1

Abstract

Öğrencilerin sınıf ve sınav salonlarında yoklamalarını almak (YA), eğitmenler için sadece külfetli bir iş değil, aynı zamanda zaman alıcıdır. YA’da verimli ve otonom tekniklere artan bir ihtiyaç vardır. Bu makale, yüz tanımaya dayalı bir yoklama sistemini tanıtmaktadır. Geliştirilen yöntem, öğrencilerin canlı kamera görüntülerinde veya belirli bir görüntüde bulunan yüzlerini Eigen Face Recognizer algoritması ile tespit etmektedir. Yüzlerinden tespit edilen öğrencilerin yoklama bilgisi bir veri tabanına kaydedilir. Tanıma sürecinde sınıflandırıcı olarak HAAR algoritması kullanılmıştır. Deneysel çalışmalarda yüz tanıma sisteminin gerçek sınıf ortamında ortalama %79.31 doğrulukla çalıştığı gözlenmiştir. Elde edilen sonuçlar, tasarlanan sistemin, sınıf ve sınav oturumlarında otomatik kimlik doğrulama ve yoklama almada umut verici olduğunu göstermiştir. Önerilen sistem ile işaretleme, doğrulama ve kayıt işlemlerinin çok daha kısa sürede ve daha yüksek doğrulukla tamamlanabileceği de gösterilmiştir.

Keywords

yoklama alma, sınıf, yüz tanıma, haar sınıflandırıcı, eigenfaces.

Rapid Marking Attendance with Face Recognition

Abstract

Marking attendance (MA) of students in the classroom and exam halls is not only a burdensome task for the instructors, but it is also time consuming. There is a growing need for efficient and autonomous techniques in AM. This article introduces an attendance system based on face recognition. The developed method detects the students exploiting their faces present in live camera images or in a given image through the Eigen Face Recognizer algorithm. After then, students are recognized and their attendance information recorded in an offline database. HAAR algorithm is used as a classifier in recognition process. In the experimental studies, it has been observed that the face recognition system works with an average accuracy of 79.31% in the real classroom environment. The obtained results showed that the designed system is promising for automatic authentication and marking attendance in classroom and exam sessions. It has been also shown that with the proposed system, marking, authentication and recording works can be completed in a much shorter time and with higher accuracy.

Keywords

attendance marking, classroom, face recognition, haar classifier, eigenfaces

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