Enhanced Forensic Face Reconstruction: A Conditional U-Net Diffusion Model Approach

Reconstructing realistic faces from forensic sketches is a critical challenge in computer vision, with significant implications for security and forensic applications. This paper introduces an advanced conditional U-Net diffusion model designed to enhance the fidelity and accuracy of facial image reconstruction from sketches. By leveraging a gradual noise reduction process, the model preserves fine structural details more effectively than traditional methods. Using the CUHK dataset, our model achieved a Fréchet Inception Distance of 1.40, a Peak Signal-to-Noise Ratio of 43.15 dB, and a Structural Similarity Index of 99.68%, demonstrating superior performance over DCGAN models. These results set a new standard for high-fidelity forensic face reconstruction, providing a robust tool for criminal identification and media restoration.