Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2379
Title: Comparing Image Similarity Methods for Face Images
Authors: Örnek, Ahmet Haydar
Çelik, Mustafa
Alper, Ozan Can
Keywords: deep learning
image similarity
real-world applications
Deep learning
'current
Deep learning
Face images
Image similarity
Learning-based methods
Real-time computer vision
Real-world
Real-world application
Image analysis
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In order to realize real-time computer vision projects we need to avoid time consuming operations such as more inference for deep learning. Our current application uses face images to decide whether there is a mask on the face so as to prevent unhealthy situations in view of epidemic. Since frames are sequentially coming it is necessary to eliminate similar frames to avoid more inference. We show how to measure a similarity between two frames by comparing traditional and deep learning based methods in this study. This study shows that deep learning based method is more efficient than traditional methods when comparing images. © 2021 IEEE.
Description: 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 -- 9 December 2021 through 10 December 2021 -- -- 177176
URI: https://doi.org/10.1109/ICECET52533.2021.9698252
https://hdl.handle.net/20.500.13091/2379
ISBN: 9781665442312
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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