Abstract:
Palmprint is one of biometric modalities, which mostly dominated in crime-scenes
and access control applications in the past. The majority of the images in access
control systems are captured by touch-based sensors using fingertips or palmprint
with low-resolution devices whereas the images in forensic scenario are captured
as latent prints using high resolution cameras. The issues originate during different
types of palmprint acquisition, demand a suitable algorithm to overcome geometric
and photometrical variations of acquired images. Full to full, full to partial and
latent to full palmprint combinations are different types matching approaches. Full
to partial or latent palmprint matching techniques are still being a challenge in
forensic applications due to lower quality and nonlinear image distortion as the
palmprints comparatively have creases, lines and a larger surface area than
fingerprints, which can generate a large number of fake minutiae when the quality
of the image is low. Therefore, conventional fingerprint-based algorithms are not
directly suitable for such images, and they need some altered tactics to be adopted
in the palmprint domain. This research explores an alternate strategy for the use of
minutiae-based algorithm where minutiae-like points are used as key features to
form a graph for matching images that exhibit large variations in quality through
geometrical transformations and partial occlusion. The algorithm is evaluated
using partial and full-degraded image segments acquired from publicly available
THUPALMLAB database. It is observed that the algorithm performs better with
partial and full-degraded palmprint segments. The goal of the algorithm design is
to propose a method that can suit for any segment of the hand print. The algorithm
proves the principles behind the methodology and demonstrates it has some
potential to extend with multiple segments of the hand prints using fusion.
