Multi-Model Biometric Authentication System for Smart Attendance
K.RaviTeja1, Brahmananda S H2, Swasthika Jain T J3
1K.RaviTeja*, CSE department, GITAM University, Bangalore, India.
2Brahmananda S H, CSE department, GITAM University, Bangalore, India.
3Swasthika Jain T J, CSE department GITAM University, Bangalore, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4284-4287 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9094038620/2020©BEIESP | DOI: 10.35940/ijrte.F9094.038620
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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: To increase the success rate in academics, attendance is an essential aspect for every student in schools and degree colleges. In olden days, this attendance is manually taken by teachers with pen and paper method, which consumes more amount of time in their busy management scheduling era. To make this attendance taking more comfortable and more accurate, a multi model biometric system for attendance monitoring system is proposed using a Raspberry Pi single-board computer. The camera and biometric device which is connected to the system gathers Information regarding the students by recognizing their faces and their fingerprint simultaneously. If both of them match with the student details stored in the database, then the system will be sending an alert about the student presence in the class. The student details which is stored into the database is collected from the students initially. By using these details like images and fingerprints the system is trained by using a Convolutional Neural Network (CNN) Machine Learning Algorithm.
Keywords: Attendance Monitoring, Raspberry Pi, Machine Learning, Database, CNN Algorithm.
Scope of the Article: Machine Learning.