From Sketch to Image: A GAN-Based Forensic Reconstruction System Using Text Prompts

Facial sketch-to-image synthesis is a crucial task in forensic investigations, digital art, and human-computer interaction. This research presents a Generative Adversarial Network (GAN)-based model that converts facial sketches into high-fidelity, photorealistic images guided by textual descriptions. The proposed approach integrates Contrastive Language-ImagePretraining (CLIP) to enhance textual feature extraction, ensuring detailed and accurate facial reconstructions. A refined encoder to StyleGAN pipeline is employed to generate images with structuralcoherenceand perceptual realism. The model is trainedon the Multi-Modal-CelebA-HQ dataset, comprising 19,923 paired sketches, images, and textual descriptions. Performance evaluationisconducted usingstandardimage similaritymetrics,includingL2Normand Structural Similarity Index (SSIM). Experimental results demonstrate that the proposed method achieves a highSSIMscore(0.788)andlowL2Norm(89.68),indicatingstrongstructuralsimilarityandfine-detail preservation. Despite promising results, limitations remain, such as the model's dependency on textual prompts, potential bias in generated features, and computational constraints. Future work will explore multi-modal enhancements, such as incorporating audio cues or additionalimage inputs, as well as adopting self-attention mechanisms and transformer-based architectures for improved image synthesis. This study contributes to advancements in AI-driven face reconstruction with applications in forensics, digital art, and entertainment industries