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

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      Sensors
      MDPI AG

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          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.

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          Image Quality Assessment: From Error Visibility to Structural Similarity

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            Image-to-Image Translation with Conditional Adversarial Networks

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              The Internet of Things: A survey

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
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                Journal
                SENSC9
                Sensors
                Sensors
                MDPI AG
                1424-8220
                December 2021
                December 08 2021
                : 21
                : 24
                : 8178
                Article
                10.3390/s21248178
                e2215826-954c-483b-8932-371a67912d90
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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