Title: Level-3 Friction Ridge Pattern Analysis
Speaker: Mayank Vatsa
Affiliation Ph.D. Candidate, West Virginia University
Date: August 22, 2008
Abstract
Fingerprint features have been widely used for verifying the identity
of an individual. Automatic fingerprint verification systems use ridge flow patterns and
general morphological information for broad classification, and minutiae information for
verification. The ridge flow patterns and morphological information are referred to as level-1
features, while ridge endings and bifurcations, also known as minutiae, are referred to as
level-2 features. With the availability of high resolution fingerprint sensors, it is now
feasible to capture more intricate features such as ridge path deviation, ridge edge features, ridge
width and shape, local ridge quality, distance between pores, size and shape of pores, position of
pores on the ridge, permanent scars, incipient ridges, and permanent flexure creases.
These fine details are characterized as level-3 features and play an important role in
matching and improving the verification accuracy.
The main objective of my PhD research is to develop a fast and
accurate automated fingerprint verification algorithm that incorporates both level-2 and level-3
features. First, a fast Mumford-Shah curve evolution algorithm is used to extract four level-3 features
namely, pores, ridge contours, dots, and incipient ridges. For improving the fingerprint verification
performance, we further propose an evidence-theoretic multimodal unification approach using belief
functions that takes into account the variability in level-2 and level-3 characteristics. Experimental
results and statistical tests on a database of 700 classes show the effectiveness of the proposed
algorithms. Compared to existing algorithms, the proposed approach is computationally efficient, and the
verification accuracy is not compromised even with partial fingerprints.
Biography
Mayank Vatsa received the M.S. degree in computer science in 2005 and
is currently working toward the Ph.D. degree in computer science at West Virginia University,
Morgantown, USA. He was actively involved in the development of a multimodal biometric system, which includes
face, fingerprint, signature, and iris recognition at Indian Institute of Technology, Kanpur, India, from
July 2002 to July 2004. He has 73 publications in refereed journals, book chapters, and conferences. His current
areas of interest include information fusion, biometrics, digital forensics, computer vision, image processing, and
belief theory. Mayank is a member of the IEEE, Computer Society and ACM. He is also a member of the Phi Kappa
Phi, Tau Beta Pi, Sigma Xi, Upsilon Pi Epsilon, and Eta Kappa Nu honor societies. He was the recipient of four best
paper awards.