Enhancement of Latent Fingerprint Images with Segmentation Perspective

Abstract

Latent fingerprints are of decisive influence when it comes to identifying suspects, as these are usually encountered at crime scenes. Also, these serve as a compelling evidence in a court of law. Some of the formidable challenges with latent fingerprints are deficient ridge information and poor ridge clarity due to background noise and non-linear distortions. An effective fingerprint enhancement scheme is suggested to meliorate the clarity and continuity of ridges and valleys in a latent fingerprint image. The proposed method deals with both local (minutia or singular points, ridge termination, bifurcation, broken ridges, short ridges, core and delta) and global features (ridge orientation and ridge frequency), while retaining the true ridge-valley structures and removing noise at the same time. The proposed design establishes scalability, accessibility and flexibility.