2 research outputs found
Risk-based neuro-grid architecture for multimodal biometrics
Recent research indicates that multimodal biometrics is the way forward for a highly reliable adoption of biometric identification systems in various applications, such as banks, businesses, government
A multiexpert collaborative biometric system for people identification
Present identification through single-biometric systems suffer from a number of
limitations, due to the fact that no single bodily or behavioral feature is able to satisfy at
the same time acceptability, speed and reliability constraints of authentication in real
applications. Multibiometric systems can solve a number of problems of singlebiometry approaches. A crucial issue to be investigated relates to how results from
different systems should be evaluated and fused, in order to obtain an as reliable as
possible global response. A further source of flaws for present systems, both singlebiometric and multibiometric, can be found in the lack of dynamic update of
parameters, which does not allow them to adapt to changes in the working settings.
They are generally calibrated once and for all, so that they are tuned and optimized with
respect to specific conditions. In this work, we investigate an architecture where singlebiometry subsystems work in parallel, yet exchanging information at fixed points,
according to the N-Cross Testing Protocol. In particular, the integrated subsystems work
on the same biometric feature, the face in this case, yet exploiting different classifiers.
Notice that such specific configuration is interesting to underline how the strengths of
one classifier can compensate for flaws of other classifiers, so that the final result is
more accurate and reliable. Moreover, parameters of each subsystem are also
dynamically optimized according to the behavior of all the others. This is achieved by
an additional component, the supervisor module, which analyzes the responses from all
subsystems and modifies the degree of reliability required from each of them to accept
the respective responses. In this way subsystems collaborate at a twofold level, both for
returning a common answer and for tuning to changing operating conditions. The paper
explores the combination of these two novel approaches, demonstrating that
component collaboration increases system accuracy and allows identifying unstable
subsystems