Face Recognition Based Attendance System For CMR College of Engineering and Technology
Kalachugari Rohini1, Sivaskandha Sanagala2, Ravella Venkata Rathnam3, Ch.Rajakishore Babu4
1Kalachugari Rohini, M.Tech, Scholar, Department of Computer Science Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India.
2Sivaskandha Sanagala, Associate Professor, Department of Computer Science Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India.
3Ravella Venkata Rathnam, Associate Professor, Department of Computer Science Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India.
4Ch. Rajakishore Babu, Associate Professor, Department of Computer Science Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 127-129 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0026028419/2019©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: In modern times, Automatic Face recognition (AFR) technologies have seen dramatic improvements in performance over the last few years. There are two reasons for this trend; the first is for saving the time in the classroom and accuracy in attendance will be maintained, and the second is availability of advanced technology it is more useful for the future generation. In simple words, it was a computer implementation for recognizing automatically whether the student is present in the classroom or not with the help of still image or video frame. We proposed an automatic attendance management system. It was completely based on face recognition and the face detection. This both detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. This research includes for Face detection Students and system is based on CNN perspectives and algorithms.
Keywords: Face Recognition, Face Detection, CNN, AFR, Deep Learning.
Scope of the Article: Deep Learning