Non Binary Fingerprint

Non-binary fingerprint analysis encompasses techniques beyond traditional binary representations, focusing on extracting richer information from fingerprint images for improved recognition and other applications. Current research emphasizes developing advanced algorithms, including deep learning models like multi-task neural networks and transformer-based architectures, to address challenges such as denoising wet or low-quality fingerprints, segmenting fingerprints within complex images (like slap images), and detecting image forgeries. These advancements are crucial for enhancing biometric security systems, improving forensic analysis of latent fingerprints, and advancing image authentication techniques.