Latent fingerprint from multiple surfaces: Database and quality analysis

Abstract

Latent fingerprints are lifted from multiple types of surfaces, which vary in material type, texture, color, and shape. These differences in the surfaces introduce significant intra-class variations in the lifted prints such as availability of partial print, background noise, and poor ridge structure quality. Due to these observed variations, the overall quality and the matching performance of latent fingerprints vary with respect to surface properties. Thus, characterizing the performance of latent fingerprints according to the surfaces they are lifted from is an important research problem that needs attention. In this research, we create a novel multi-surface latent fingerprint database and make it publicly available for the research community. The database consists of 551 latent fingerprints from 51 subjects lifted from eight different surfaces. Using existing algorithms, we characterize the quality of latent fingerprints and compute the matching performance to analyze the effect of different surfaces.