Researchers in
the Biometric Technologies Laboratory at the University of Calgary have
developed a way for security systems to combine different biometric
measurements—such as eye color, face shape, or fingerprints—and create a
learning system that simulates the brain in making decisions about information
from different sources.

Professor
Marina Gavrilova, the founding head of the laboratory—among the first in the
research community to introduce and study neural network based models for
information fusion—says they have developed a biometric security system that
simulates learning patterns and cognitive processes of the brain.

“Our goal is
to improve accuracy and as a result improve the recognition process,” says
Gavrilova. “We looked at it not just as a mathematical algorithm, but as an
intelligent decision making process and the way a person will make a decision.”

The algorithm
can learn new biometric patterns and associate data from different data sets,
allowing system to combine information, such as fingerprint, voice, gait, or
facial features, instead of relying on a single set of measurements.

The key is in
the ability to combine features from multiple sources of information, prioritize
them by identifying more important/prevalent features to learn, and adapt the
decision making to changing conditions such as bad quality data samples, sensor
errors, or an absence of one of the biometrics.

“It’s a kind
of artificial intelligence application that can learn new things, patterns, and
features,” Gavrilova says. With this new multidimensional approach, a security
system can train itself to learn the most important features of any new data
and incorporate it in the decision making process.

“The neural
network allows a system to combine features from different biometrics in one,
learn them to make the optimal decision about the most important features, and
adapt to a different environment where the set of features changes. This is a
different, more flexible approach.”

Biometric information is becoming more common in our daily lives, being
incorporated in drivers’ licenses, passports, and other forms of
identification. Gavrilova says the work in her laboratory is not only
pioneering the intelligent decision-making methodology for human recognition
but is also important for maintaining security in virtual worlds and avatar
recognition.

Source: University of Calgary