INTRODUCTION Latent fingerprints are impressions of fingers lifted from crime scenes. Matching latents to plain or rolled fingerprints that are stored in law enforcement databases is a very challenging task. Latents are usually poor quality images – smudgy, blurred, with small area, background noise, and small number of minutiae.

It is very difficult to automatically extract reliable features from latents. Features in latents such as minutiae, region of interest (ROI), singular points, are typically manually marked by latent experts. Our goal is to improve latent matching accuracy by using as few manually marked features as possible.

Fig. 1: Examples of latents with manually marked minutiae

PROPOSED APPROACH

Fig. 2: Overview of latent fingerprint matching approach

LATENT FINGERPRINT MATCHING USING DESCRIPTOR-BASED HOUGH TRANSFORM

Department of Computer Science &

Engineering

Alessandra A. Paulinoa, Jianjiang Fengb, Anil K. Jaina aDept. Of Computer Science and Engineering. Michigan State University, East Lansing, MI, USA,

bDept. Of Automation, Tsinghua University, Beijing, China paulinoa@cse.msu.edu, jfeng@tsinghua.edu.cn, jain@cse.msu.edu

REFERENCE [1] Cappelli R., Ferrara M., Maltoni D., “Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition", IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010.

ACKNOWLEDGMENTS Alessandra Paulino's research was supported by the Fulbright Program (A15087649) and the Brazilian Government through a CAPES Foundation/Ministry of Education grant (1667-07-6).

LATENT MATCHING APPROACH Input to matcher: manually marked minutiae in latents and automatically extracted minutiae in rolled prints. Alignment: each possible minutiae pair votes for a set of alignment parameters (translation and rotation); vote is proportional to the similarity between their local minutiae descriptors1 in a coarse parameter space; detected peaks in Hough space are further refined and another voting is performed in a local neighborhood of these peaks; one highest peak per fine parameter space is chosen as a candidate alignment. Minutiae Pairing: a minutia pair (from aligned latent and rolled print) is considered a matched pair if the Euclidean distance and direction difference between the two are less than some pre-specified thresholds (one-to-one minutiae matching). Score Computation: global matching score for each candidate alignment is the sum of the similarities of all matched pairs divided by the number of minutiae in the latent; final score between a latent and rolled print is the maximum score among the candidate alignments.

1

SUMMARY AND FUTURE WORK The proposed alignment scheme leads to performance improvement (~16%) over a commercial matcher. Ongoing work: improve the matching performance by matching orientation field reconstructed from minutiae points.

EXPERIMENTAL RESULTS Database: 258 latent fingerprints (from NIST Special Database 27) and 27,258 rolled prints (258 mated rolled prints from NIST Special Database 27 and 27,000 rolled prints from NIST Special Database 14)

Our approach outperforms the commercial matcher VeriFinger due to the better performance of our descriptor-based alignment, which also leads to a better minutiae pairing.

Fig. 3: Cumulative Match Characteristic (CMC) curves

Table 1: Rank-1 accuracies for subjective and objective qualities of latents in NIST SD27.

Fig. 4: Successful match, even with small no. of minutiae

Fig. 5 Unsuccessful match due to missing minutiae in rolled print

Quality VeriFinger (%) Proposed Matcher (%) All 38.8 55.4

Subjective Good 55.7 73.9 Bad 38.8 48.2 Ugly 21.2 43.5

Objective Large 61.6 74.4

Medium 43.5 55.3 Small 11.5 36.8