Examining a Deep Learning Network System for Image Identification and Classification for Preventing Unauthorized Access for a Smart Home Security System
Document Type
Article
Publication Date
2019
Publication Title
Issues in Information Systems
Abstract
There are many different smart home surveillance and control systems, which will need some type of visual identification and classification system. Past models of Deep Learning have had great success in visual identification and image classification particularly in the healthcare and security industries. This study reviews past architecture and applications of Deep Learning and Convolutional Neural Networks. This paper then presents the creation, process, testing, and results of a CNN model with the end objective of identifying images for determination of access rights. Evaluation outcomes show that after 50 forward and backward dataset training passes the deep learning network achieved an identification accuracy of 96.7% and a 98.0% probability of proper classification of access authorization. The results suggest that deep learning models could be successful in strengthening smart home security systems.
Recommended Citation
Egbedion, Beloved, Hayden Wimmer, Carl Rebman, Loreen Marie Powell.
2019.
"Examining a Deep Learning Network System for Image Identification and Classification for Preventing Unauthorized Access for a Smart Home Security System."
Issues in Information Systems, 20 (3): 107-116: International Association for Computer Information Systems.
source: http://www.iacis.org/iis/2019/3_iis_2019_107-116.pdf
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/99
Copyright
© Copyright, IACIS 2020
