Precision sketching with de-aging networks in forensics

Abstract

Addressing the intricacies of facial aging in forensic facial recognition, traditional sketch portraits often fall short in precision. This study introduces a pioneering system that seamlessly integrates a de-aging module and a sketch generator module to overcome the limitations inherent in existing methodologies. The de-aging module utilizes a deepfake-based neural network to rejuvenate facial features, while the sketch generator module leverages a pix2pix-based Generative Adversarial Network (GAN) for the generation of lifelike sketches. Comprehensive evaluations on the CUHK and AR datasets underscore the system’s superior efficiency. Significantly, comprehensive testing reveals marked enhancements in realism during the training process, demonstrated by notable reductions in Frechet Inception Distance (FID) scores (41.7 for CUHK, 60.2 for AR), augmented Structural Similarity Index (SSIM) values (0.789 for CUHK, 0.692 for AR), and improved Peak Signal-to-Noise Ratio (PSNR) metrics (20.26 for CUHK, 19.42 for AR). These findings underscore substantial advancements in the accuracy and reliability of facial recognition applications. Importantly, the system, proficient in handling diverse facial characteristics across gender, race, and culture, produces both composite and hand-drawn sketches, surpassing the capabilities of current state-of-the-art methods. This research emphasizes the transformative potential arising from the integration of de-aging networks with sketch generation, particularly for age-invariant forensic applications, and highlights the ongoing necessity for innovative developments in de-aging technology with broader societal and technological implications.