Face Recognition Based Attendance System
Nandhini R1, Duraimurugan N2, S.P.Chokkalingam3
1Nandhini R, Assistant Professor, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
2Duraimurugan N, UG Student Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
3S.P.Chokkalingam, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 11 June 2019 | Manuscript Published on 22 June 2019 | PP: 574-577 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C11230283S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Automatic face recognition (AFR) technologies have made many improvements in the changing world. Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day to day activities of handling student attendance system. Face recognition-based attendance system is a process of recognizing the students face for taking attendance by using face biometrics based on high – definition monitor video and other information technology. In my face recognition project, a computer system will be able to find and recognize human faces fast and precisely in images or videos that are being captured through a surveillance camera. Numerous algorithms and techniques have been developed for improving the performance of face recognition but the concept to be implemented here is Deep Learning. It helps in conversion of the frames of the video into images so that the face of the student can be easily recognized for their attendance so that the attendance database can be easily reflected automatically.
Keywords: Face Recognition, Face Detection, Deep Learning, Convolution Neural Network(CNN).
Scope of the Article: Pattern Recognition