An Intelligent Faculty Tracking and Attendance Marking System using Machine Learning
Hariharan R L1, Elza Susan Aby2, Abin Alex Joe3, Reshmi K S4, Jicku Philip Varughese5
1Hariharan R L, Assistant Professor, Department of Computer Science and Engineering, Providence College of Engineering, Chengannur, India.
2Elza Susan Aby, UG Scholar, Department of Computer Science and Engineering, Providence College of Engineering, Chengannur, Kerala, India.
3Abin Alex Joe, UG Scholar, Department of Computer Science and Engineering, Providence College of Engineering, Chengannur, Kerala, India.
4Reshmi K S, UG Scholar, Department of Computer Science and Engineering, Providence College of Engineering, Chengannur, Kerala, India.
5Jicku Philip Varughese, UG Scholar, Department of Computer Science and Engineering, Providence College of Engineering, Chengannur, Kerala, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7987-7994 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4379118419/2019©BEIESP | DOI: 10.35940/ijrte.D4379.118419

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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 the current era, machine learning and IoT have grown vastly and is one of the most important technologies used today. It has various applications like automated systems, driverless cars, fire alarming system, etc. In this project we are going to implement an intelligent attendance marking system and how to track a faculty in school/college using face detection, i.e., by using machine learning algorithm for attendance marking and using RFID, IP camera, GPS technology for tracking the location of the faculty in the school/college. This system consists of two modules of operations. The first module is the attendance marking module and the second module is tracking the location. This system checks for the location of the faculty when the user enters the name to be searched in the app. The system first checks for the faculty in closed areas like the classroom, canteen, library, etc. and then open areas like the ground, parking area, etc. If the faculty’s location is not identified then the system checks the attendance data and informs whether the faculty is absent or not to the user. This methodology has many future scopes. If the machine contains precise data it becomes less time-consuming, it can improve its accuracy and produce exact results and it can reduce the human effort. It can replace the biometric system for attendance marking.
Keywords: Human Tracking, Haar Algorithm, Intelligent Attendance Marking System, Radio Frequency Identification (RFID), Global Positioning System (GPS), Internet Protocol (IP) Camera..
Scope of the Article: Machine Learning.