Automatic Student Attendance System using Face Recognition
Partha Chakraborty1, Chowdhury Shahriar Muzammel2, Mahmuda Khatun3, Sk. Fahmida Islam4, Saifur Rahman5
1Partha Chakraborty*, Department of CSE, Comilla University, Comilla, Bangladesh.
2Chowdhury Shahriar Muzammel, Department of CSE, Comilla University, Comilla, Bangladesh.
3Mahmuda Khatun, Department of CSE, Comilla University, Comilla, Bangladesh.
4Sk. Fahmida Islam, Department of CSE, Jahangirnagar University, Savar, Dhaka, Bangladesh.
5Saifur Rahman, Department of CSE, Comilla University, Comilla, Bangladesh.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 93-99 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4207129219/2020©BEIESP | DOI: 10.35940/ijeat.B4207.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The most common difficulty that every teacher faces in class room is to take the attendance of the students one by one in each and every class. For the time being many automated systems has been proposed for taking student attendance. In this paper, I introduced an automated student attendance system based on the use of unique techniques for face detection and recognition. This system automatically detects the student when he or she enters the classroom and recognizes that specific student and marks the student’s attendance. This method also focuses on the specific features of different attributes such as the face, eye and nose of humans. In order to evaluate the performance of different face recognition system, different real-time situations are considered. This paper also suggests the technique for handling the technique such as spoofing and avoiding student proxy. This system helps track students compared to traditional or current systems and thereby saves time.
Keywords: Face detection, Feature Extraction, Face recognition, Eigenface, Haar Cascade Classifier, Principal Component Analysis (PCA)