A Self-updating Multiexpert System for Face Identification

Multibiometric systems can solve a number of problems of single-biometry approaches. A source of flaws for present systems, both single-biometric 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 standard conditions. In this work we investigate an architecture where single-biometry 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. Subsystems collaborate at a twofold level, both for returning a common answer and for tuning to changing operating conditions. Results demonstrate that component collaboration increases system accuracy and allows identifying unstable subsystems.