Smart attendance maintenancesystem has been a research topic from past a fewdecades; each method has its own disadvantages and advantages.An algorithm using Convolutional Neural Network and Image processinghas been proposed in this paper to overcome the disadvantages of the previous algorithms. Image recognition is playing an important role in the modern living like driver assistance systems, medical imaging system, quality control system to name a few. An Artificial Neural Network along with image recognition used to enhance the reliability of the attendancesystem. One such update used here is CNN.Deep learning has been an emerging technology hence opted to implement the smart attendance system.The implementation basically consists of three components : 1)Face scanning and detection using HAAR cascade method 2)Training the CNN-ANN model 3)Recognize the face and update the attendance .The main motivation of our work is to merge three of the emerging technologies : Machine learning , Image Processing and IOT . Key advantage of this implementation is that a deep learning model increases its accuracy with more epochs of training andit optimizes the run time.
M. K. Yeop Sabri, M. Z. A. Abdul Aziz, M. S. R. Mohd Shah, M. F Abd Kadir, "Smart Attendance System by suing RFID", Applied Electromagnetics 2007. APACE 2007. Asia -Pacific Conference on, 4–6 Dec. 2007.
R. Apoorv, P. Mathur, "Smart attendance management using Bluetooth Low Energy and Android", 2016 IEEE Region 10 Conference (TENCON), 2016.K.
N. I. Zainal, K. A. Sidek, T. S. Gunawan, H. Mansor, M. Kartiwi, "Design and Development of Portable Classroom Attendance System Based on Arduino and Fingerprint Biometric", proc. of The IEEE 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M), pp. 1-4, 17–18 Nov. 2014.
K.Lakshmi Sudha, Shirish Shinde, Titus Thomas, “Barcode based student attendance system” International Journal of Computer Applications (0975 – 8887) Volume 119 – No.2, June 2015.
S. Dey, S. Barman, R. K. Bhukya, R. K. Das, B C Haris, S. R. M. Prasanna, R. Sinha, "Speech biometric based attendance system", National Conference on Communications, 2014.
MuhammetBaykar, UğurGürtürk, ErtuğrulKarakaya, “NFC based smart mobile attendance system”, 2017 International Conference on Computer Science and Engineering (UBMK)
Mrs. Madhuram.M, B. Prithvi Kumar, Lakshman Sridhar, Nishanth Prem, Venkatesh Prasad “Face Detection and Recognition Using OpenCV”.
Sander Soo Institute of Computer Science, University of Tartu “Object detection using Haar-cascade Classifier”. https://pdfs.semanticscholar.org/0f1e/866c3acb8a10f96b432e86f8a61be5eb6799.pdf
Sun Ichi Amari “Backpropagation and stochastic gradient descent method” National Conference on Deep learning, 2017.
Zhipeng yu and Jie yang‘s “Brain MRI segmentation with patch-based CNN approach” conference paper, July 2016. https://www.researchgate.net/publication/308496682_Brain_MRI_segmentation_with_patch-based_CNN_approach/citation/download
Wagh, J Priyanka, Agruti Chaudhari, Roshani Thakare, Shweta Patil, "Attendance system based on face recognition using eigen face and PCA algorithms", Green Computing and Internet of Things (ICGCIoT) 2015 International Conference on, pp. 303-308, 2015.
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