Pradhan, M R, Elroy Martis, Jason, Manjaya Shetty, Sannidhan, Desai, Usha and Acharya, Biswaranjan (2023) Text-to-Sketch Synthesis via Adversarial Network. Computers, Materials & Continua, 76 (1). pp. 915-938. ISSN 1546-2226
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Abstract
In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided by an observer. Additionally, we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated sketches. To evaluate the effectiveness of our proposed method, we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance metrics. Our proposed method achieved an overall inception score of 1.438 ± 0.049 and a Frechet Inception Distance of 65.29, outperforming other state-of-the-art techniques.
Affiliation: | Skyline University College |
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SUC Author(s): | Pradhan, M R ORCID: https://orcid.org/0000-0002-0115-2722 |
All Author(s): | Pradhan, M R, Elroy Martis, Jason, Manjaya Shetty, Sannidhan, Desai, Usha and Acharya, Biswaranjan |
Item Type: | Article |
Uncontrolled Keywords: | Generative adversarial networks; inverse gamma correction; sketch attributes; text-to-sketch synthesis; deep learning techniques |
Subjects: | B Information Technology > BR Deep Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Dec 2023 13:40 |
Last Modified: | 25 Dec 2023 13:40 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/729 |
Publisher URL: | https://doi.org/10.32604/cmc.2023.038847 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/37365 |
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