Today the science of biometrics is
merging with the field of information technology to create new methods
for collecting data on a variety of other human physical and behavioral
traits as well. Innovative devices and technologies capable of
analyzing such traits as retinas, irises, voice patterns, facial
features, and hand measurements are already being used for biometric
authentication across a wide array of
areas including corporate and public security systems, military
surveillance, counter-terrorism initiatives, and point of sale
applications.
Leading the way in the development of new biometric technology is Dr. Marios Savvides,
Director of the CyLab Biometrics Lab. With funding from CyLab,
Savvides and his team of researchers are breaking new ground in the
enhancement of existing biometric authentication technologies as well as
the creation of revolutionary new ones. Savvides’ work is specifically
geared toward improving the use of iris and face recognition in
biometric authentication systems.
Savvides’ work in iris recognition focuses on two problems inherent
to its use as a means of biometric authentication: image quality and
device efficiency. Images collected with iris recognition devices are
frequently of low quality, containing random specular reflections in and
around the pupil and iris that impact the performance of iris
segmentation algorithms. To improve image quality, Savvides developed a
more robust iris segmentation algorithm capable of segmenting an image
of the iris in an average time of 3.37 seconds, versus an average time
of 15.99 seconds using the old algorithm. Reduced segmentation time
yields fewer instances of specular reflection in iris images and lowers
the iris segmentation error rate from 9.65% to 3.66%.
Savvides has also focused on enhancing device efficiency. Most iris
recognition devices can capture only one image of an iris at a time.
After each image capture, the device user must manually enter several
pieces of identifying information, including whether the image is of a
left eye or a right eye. The single capture ability of iris recognition
devices slows the data collection process and increases the likelihood
that iris images will be misidentified and mislabeled. In response to
these problems, Savvides developed an iris recognition algorithm capable
of detecting left eyes from right eyes, allowing devices to capture
images of both irises simultaneously. Tests of the new algorithm on
four different iris databases have shown it to be highly efficient,
adding no additional computation time to current iris recognition
systems and with a 99% identification accuracy rate.
(Source: CyLab)
