Kushal Kumar Jain supervised by Prof. Anoop Namboodiri  received his Master of Science – Dual Degree  in Computer Science and Engineering (CSD). Here’s a summary of his research work on  Face Sketch Generation and Recognition:

 The field of sketch generation and recognition has seen significant advancements through the innovative application of generative models. This thesis presents a comprehensive exploration of face stylization , artistic portrait generation and forensic sketch synthesis, leveraging the power of state of the art generative models like StyleGAN and StableDiffusion. Our work addresses key challenges in preserving identity, accommodating various poses, and bridging the modality gap between sketches and photographs. Through three interconnected studies, we demonstrate significant advancements in generating high-quality sketches and improving forensic applications. We begin by introducing a novel approach to face cartoonization, that preserves identity and accommodates various poses. Unlike conditional-GAN methods, our technique utilizes an encoder to capture pose and identity information, generating embeddings within StyleGAN’s latent space. This approach uniquely adapts a pre-trained StyleGAN model, originally designed for realistic facial images, to produce cartoonized outputs without requiring a dedicated fine-tuned model. Building upon this foundation, we present Portrait Sketching StyleGAN (PS StyleGAN), a style transfer approach tailored for portrait sketch synthesis. PS StyleGAN leverages StyleGAN’s semantic W+ latent space to generate portrait sketches while allowing meaningful edits such as pose and expression alterations. By introducing Attentive Affine transform blocks and a specialized training strategy, we achieve high-quality sketch generation without fine-tuning StyleGAN itself. This method demonstrates superior performance over current state-of-the-art techniques, requiring only a small number of paired examples and minimal training time. Finally, we address the challenging task of forensic sketch-to-mugshot matching with CLIP4Sketch, a novel approach utilizing diffusion models to generate diverse sketch images. By combining CLIP and Adaface embeddings of reference mugshots with textual style descriptions, we create a comprehensive dataset of sketches corresponding to mugshots. This synthetic data significantly improves sketch-to-mugshot matching accuracy in face recognition systems, outperforming training on limited real face sketch data and datasets made by GAN-based methods. Collectively, these contributions push the boundaries of sketch generation and recognition, offering promising applications in both artistic and forensic domains.

May 2025