Latent Fingerprint

Latent fingerprint analysis focuses on identifying individuals from partial or degraded fingerprints left at crime scenes, a challenging task due to image quality issues. Current research heavily employs deep learning, particularly generative adversarial networks (GANs) and transformer architectures, to enhance fingerprint images by restoring ridge patterns and accurately locating minutiae points, often incorporating both local and global feature representations for improved matching accuracy. These advancements significantly impact forensic science by improving the reliability and speed of fingerprint identification in criminal investigations, leading to more robust and efficient forensic analysis.