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A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Title
A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence
Authors
Azhar, IrfanSharif, MuhammadRaza, MudassarKhan, Muhammad AttiqueYong, Hwan-Seung
Ewha Authors
용환승
SCOPUS Author ID
용환승scopus
Issue Date
2021
Journal Title
SENSORS
ISSN
1424-8220JCR Link
Citation
SENSORS vol. 21, no. 24
Keywords
smart citiessketch synthesisconvolutional neural networkVgg-19 netU-NetSpiral-Netface recognitionNLDAOpenBR
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo-sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.
DOI
10.3390/s21248178
Appears in Collections:
인공지능대학 > 컴퓨터공학과 > Journal papers
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