Smart attendance system using Convolution Neural Network and Image Processing

Senigala Kuruba ChayaDevi and Vamsi Agnihotram
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Senigala Kuruba ChayaDevi: Department of Information Technology, Vasavi College of Engineering, India
Vamsi Agnihotram: Department of Information Technology, Vasavi College of Engineering, India

European Journal of Engineering and Technology Research, 2020, vol. 5, issue 5, 611-616

Abstract: 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.

Keywords: Deep Learning; HAAR Cascade Classifier; Convolution Neural Network (ConvNet); Artificial Neural Network (ANN); Internet of Things (IoT) (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:5:y:2020:i:5:id:61865

DOI: 10.24018/ejeng.2020.5.5.1865

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