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@article{171761,
        author = {Pranav Asane and Shantanu Kharade and Shubham Tapale and Neeraj Kalambe and Priti Malkhede},
        title = {Face Sketch to Image Generation using Hybrid  GAN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1357-1361},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171761},
        abstract = {Creating lifelike facial images from hand-drawn sketches is a complex task with broad applications, ranging from artistic and design purposes to criminal investigations. This paper presents an innovative web application powered by a hybrid Generative Adversarial Network (GAN) model, combining the strengths of DCGAN and Cycle-GAN for superior image synthesis. Users can seamlessly upload sketches, generate high-quality images, and download results, supported by real-time processing feedback. The architecture, featuring an intuitive interface and advanced GAN training methods, is discussed alongside evaluation metrics for image fidelity. This approach opens new avenues for digital creativity and practical use in law enforcement.},
        keywords = {Sketch-to-image generation, Generative Adversarial Networks, Hybrid GANs, Image synthesis, Web- based tools.},
        month = {January},
        }