Detecting Fake Faces in smart Cities Security surveillance using Image Recognition and Convolutional Neural Networks

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Published Sep 8, 2021
Venkata daya sagar Ketaraju

Abstract

There are expected to be millions of sensors and devices connected to the Internet in intelligent cities. Sensors in a variety of applications can generate a large volume of data. Connected cars are an important element of an intelligent city. Citizen safety is an important element of quality of life in a Smart City in new urban environments. The safety issue has been a significant concern for everyone for a long time. A violation of safety in private spaces has become a danger for all to stop. If traditional security systems feel a violation of safety, they sound a warning. Image processing in combination with a thorough understanding of convolutional neural networks to identify and classify images helps recognize a violation of an advanced model, thereby significantly improving future protection. Thanks to the ability to remove complex characteristics from images with accurate algorithms for facial and body detection. The output of specific machine learning is exceptional, particularly deep learning transition. In every field of science and technology, the use of such technologies to advance current systems and models will be an essential step forward. The two can do much more than is thought feasible when combined and used in the area of defence, and this paper seeks to do the same.

How to Cite

Ketaraju, V. daya sagar. (2021). Detecting Fake Faces in smart Cities Security surveillance using Image Recognition and Convolutional Neural Networks. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/201
Abstract 8 |

Article Details

Keywords

Smartcities,Sensornetworks, SVM,CNN, classification, Fake image detection

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Section
GE3- Computers & Information Technology