From Sketch to Image: A GAN-Based Forensic Reconstruction System Using Text Prompts
Year: 2025
Type: article
Abstract: 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
Source: Journal of Propulsion Technology
Author: Tuijin Jishu/Journal of Propulsion Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W6968829998
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