Issue No. 04 - April (2013 vol. 35)
Jianjiang Feng , Dept. of Autom., Tsinghua Univ., Beijing, China
Jie Zhou , Dept. of Autom., Tsinghua Univ., Beijing, China
A. K. Jain , Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint orientation field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.
Estimation, Dictionaries, Feature extraction, Noise measurement, Smoothing methods, Noise, Mathematical model
Jianjiang Feng, Jie Zhou and A. K. Jain, "Orientation Field Estimation for Latent Fingerprint Enhancement," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 4, pp. 925-940, 2013.