Automated clarity and quality assessment for latent fingerprints

Clarity of a latent impression is defined as the discern-ability of fingerprint features while quality is defined as the amount (number) of features contributing towards matching. Automated estimation of clarity and quality at local regions in a latent fingerprint is a research challenge and has received limited attention in the literature. Local clarity and quality helps in better extraction of features and assessing the confidence of matches. The research focuses on (i) developing an automated local clarity estimation algorithm, (ii) developing an automated local quality estimation algorithm based on clarity, and (iii) understanding the correlation between clarity and quality in latent fingerprints. Local clarity assessment is performed using a 2-D linear symmetric structure tensor. The goodness of orientation field is proposed to estimate the local quality of a latent fingerprint. Experiments on the NIST SD-27 database show that incorporating local clarity information in the quality assessment improves the performance of the matching system.