We present an algorithm functioning as a supervisor module in a multi expert decision making machine. It uses the Bayes theory in order to estimate the biases of individual expert opinions. These are then used to calibrate and conciliate expert opinions to one opinion. We present a framework for simulating decision strategies using expert opinions whose properties are easily modifiable. By using real data coming from a person authentication system using image and speech data we were able to confirm that the proposed supervisor improves the quality of individual expert decisions by reaching success rates of 99.5 %.

The elements of multimodal authentication along with system models are presented. These include the machine experts as well as machine supervisors. In particular fingerprint and speech based systems will serve as illustration of a mobile authentication application. A novel signal adaptive supervisor, based on the input biometric signal quality is evaluated. Experimental results on data collected from mobile telephones are reported demonstrating the benefits of the proposed scheme .

An on-line signature verification system exploiting both local and global information through decision-level fusion is presented. Global

A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-o# coe#cients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits of using the two modalities as compared to only using one of them are revealed. This is achieved by using a novel experimental procedure in which multi-modal verification performance tests are compared with multi-probe tests of the individual subsystems. Appropriate selection of the parameters of the proposed quality-based scheme leads to a quality-based fusion scheme outperforming the raw fusion strategy without considering quality signals. In particular, a relative improvement of 18% is obtained for small SVM training set size by using only fingerprint quality labels.

The use of quality information on automatic recognition systems is studied. From an apparent definition of what constitutes a quality measure, a framework for the successful exploitation of the quality information is derived. Potential applications are also introduced at different phases of the recognition process, namely: enrollment, scoring and multi-level fusion stages. Traditional likelihood scoring stage is further developed providing guidelines for the practical application of the proposed ideas. Preliminary experiments corroborate the benefits of the proposed quality-guided recognition approach. In particular, a frame-level quality measure meeting a goodness criterion based on deviation from the fundamental frequency is used, obtaining encouraging initial results.

This paper deals with the elements of a multi-modal person authentication systems. Test procedures for evaluating machine experts as well as machine supervisors based on leave-one-out principle are described. Two independent machine experts on person authentication are presented along with their individual performances. These experts consisted of a face (Gabor features) and a speaker (LPC features) authentication algorithm trained on the M2VTS multi-media database. The expert opinions are combined yielding far better performances by using a trained supervisor based on Bayesian statistics than individual modalities aggregated by averaging.

The use of quality information for multilevel speaker recognition systems is addressed in this contribution. From a definition of what constitutes a quality measure, two applications are proposed at different phases of the recognition process: scoring and multi-level fusion stages. The traditional likelihood scoring stage is further developed providing guidelines for the practical application of the proposed ideas. Conventional user-independent multilevel Support Vector Machine (SVM) score fusion is also adapted for the inclusion of quality information in the fusion process. In particular, quality measures meeting three different goodness criteria: SNR, F0 deviations and the ITUP.563 objective speech quality assessment are used in the speaker recognition process. Experiments carried out in the Switchboard-I database assess the benefits of the proposed quality-guided recognition approach for both the score computation and score fusion stages.

Humans are excellent experts in person recognition and yet they do not perform excessively well in recognizing others only based on one modality such as single facial image. Experimental evidence of this fact is reported concluding that even human authentication relies on multimodal signal analysis. The elements of automatic multimodal authentication along with system models are then presented. These include the machine experts as well as machine supervisors. In particular, fingerprint and speech based systems will serve as illustration. A signal adaptive supervisor based on the input biometric signal quality is evaluated.