Automatic Attendance System Using Extreme Learning Machine
Ankit Dalal1, Priyanka Dalal2, Sahil Dalal3
1Ankit Dalal, Department of ECE, Guru Jambheshwar University of Science and Technology, Hisar (Haryana), India.
2Priyanka Dalal, Assistant Professor, Department of ECE, Guru Jambheshwar University of Science and Technology, Hisar (Haryana), India.
3Sahil Dalal, Research Scholar, University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, Sector 16-C, Dwarka, New Delhi, India
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 880-884 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7219068519/19©BEIESP
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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: Attendance is a way of knowing whether the person is present at the place or not. This is done manually i.e. by calling the names and using biometric systems. Both these methods are time-consuming and an individual face lots of waste in their time. Therefore, to prevent this loss, a novel technique is introduced, which is based on the recognition of face of an individual for the attendance. This is done by detecting the face of the individual in some event or in a classroom, in case of students and the detected face region is matched with the stored database.Here, this is done with the help of ViolaJones algorithm for the face region detection and extreme learning machine algorithm for the matching of the detected face with the stored database. The results are observed over self-made database of few students with quite promising performance.
Keywords: Deep learning, ELM, Automatic Attendance System, Viola Jones, Detection.
Scope of the Article: Deep learning