Student Smart Attendance Through Face Recognition using Machine Learning Algorithm
Nandhini R1, Kumar P2
1Nandhini R, M.E. Student, Department of Computer Science and Engineering, Rajalakshmi Engineering College (Autonomous), Thandalam, Chennai.
2Dr Kumar P, Professor, Department of omputer Science and Engineering, Rajalakshmi Engineering College (Autonomous), Thandalam, Chennai.
Manuscript received on May 20, 2020. | Revised Manuscript received on May 22, 2020. | Manuscript published on May 30, 2020. | PP: 2348-2352 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2927059120/2020©BEIESP | DOI: 10.35940/ijrte.A2927.059120
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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: In today’s competitive world, with very less classroom time and increasing working hours, lecturers may need tools that can help them to manage precious class hours efficiently. Instead of focusing on teaching, lecturers are stuck with completing some formal duties, like taking attendance, maintaining the attendance record of each student, etc. Manual attendance marking unnecessarily consumes classroom time, whereas smart attendance through face recognition techniques helps in saving the classroom time of the lecturer. Attendance marking through face recognition can be implied in the classroom by capturing the image of the students in the classroom via the camera installed. Later through the HAAR Cascade algorithm and MTCNN model, face region needs to be taken as interest and the face of each student is bounded through a bounding box, and finally, attendance can be marked into the database based on their presence by using Decision Tree Algorithm.
Keywords: Face Recognition, Machine Learning (ml), haar cascade, opencv, Multi Task Cascaded Convolution Neural network, decision tree.
Scope of the Article: Machine Learning