Face Sketch to Image Generation using Generative Adversarial Network

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7706
Keywords:
CNN, GAN, Conditional GAN, Feature detection

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Anup Vibhute
Professor, Department of E & TC Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India
Pranita Bhosale
Assitant Professor, Department of E & TC Engineering, Army Institute of Technology, Dhighi Hills, Pune, India
Nikita Maralbhavi
Assitant Professor, Department of E & TC Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India
Shailesh Galande
Assitant Professor, Department of Computer Engineering, Pimpri Chinchwad College Of Engineering, Nigdi Pradhikaran, Pune, India
Mokshada S. Bhandare
Research Scholar, Department of E & TC Engineering, SVERI’s College of Engineering, Pandharpur

Abstract

Numerous studies have been conducted in the area of sketch to picture conversion and they got the good outcomes, but sometimes it is not accurate that they observed the blurry boundaries, the mixing of two colors that is the color of hair and face or mixing of both. These results are of the convolution neural networks that are basic of GAN. So to overcome their drawbacks we proposed a novel generative adversarial network using conditional GAN. For that we converted the original image in sketch and both the sketch and original image as reference is applied as input. We got more realistic and sharp colored images as compared to other. We focused on the feature detection, and the results are good. For the experimentation we used the STL-10 dataset. We overcome the problem of mixing of colors and got the different colors for hair, lips, and skin using conditional GAN as compared to CNN modern with increased performance and precision.

Article Details

How to Cite
Vibhute, A. ., Bhosale, P. ., Maralbhavi, N. ., Galande, S. ., & Bhandare, M. S. . (2023). Face Sketch to Image Generation using Generative Adversarial Network. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8s), 796–802. https://doi.org/10.17762/ijritcc.v11i10s.7706
Issue
Vol. 11 No. 8s (2023)
Section
Articles

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