A minutiae-based indexing algorithm for latent palmprints

Abstract

Today, many countries rely on biometric traits for individual authentication, necessitating at least one high-quality sample from each person. However, countries with large populations like China and India, as well as those with high visitor and tourist volumes like France, face challenges such as data storage and database identification. Latent palmprints, comprising about one-third of prints recovered from crime scenes in forensic applications, require inclu sion in law enforcement and forensic databases. Unlike fingerprints, palmprints are larger, and features such as minutiae are approximately ten times more abundant, accompanied by more prominent and wider creases. Consequently, accurately and efficiently identifying la tent palmprints within stored reference palmprints poses significant challenges. Using fre quency domain approaches and deep convolutional neural networks (DCNNs), we present a new palmprint segmentation method in this work that can be used for both latent and full impression prints. The method creates a binary mask. Additionally, we introduce a palmprint quality estimation technique for latent and full impression prints. This method involves parti tioning each palmprint into non-overlapping blocks and considering larger windows centered on each block to derive frequency domain values, effectively accounting for creases and en hancing overall quality mapping. Furthermore, we present a region-growing-based palmprint enhancement approach, starting from high-quality blocks identified through our quality es timation method. Similar to the quality estimation process, this method operates on blocks and windows, transforming high-quality windows into the frequency domain for processing before reverting to the spatial domain, resulting in improved neighboring block outcomes. Finally, we propose two distinct minutiae-based indexing methods and enhance an existing matching-based indexing approach. Our experiments leverage three palmprint datasets, with only one containing latent palmprints, showcasing superior accuracy compared to existing methods