We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.

The contribution of this paper is to compare paradigms coming from the classes of parametric, and non-parametric techniques to solve the decision fusion problem encountered in the design of a multi-modal biometrical identity verification system. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A decision fusion module receiving as input the d scores has to take a binary decision: accept or reject the claimed identity. We have solved this fusion problem using parametric and non-parametric classifiers. The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities. Keywords: Multi-modal identity verification, biometrics, decision fusion. 1 Introduction The automatic verification 1 of a person is more and...

The contribution of this paper is to compare paradigms coming from the classes of parametric, and non-parametric techniques to solve the decision fusion problem encountered in the design of a multi-modal biometrical identity verification system. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A decision fusion module receiving as input the d scores has to take a binary decision: accept or reject the claimed identity. We have solved this fusion problem using parametric and non-parametric classifiers. The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities. Keywords: Multi-modal identity verification, biometrics, decision fusion. 1 Introduction The automatic verification 1 of a person is more and...

Abstract not found

In this paper, the use of clustering algorithms for decision level data fusion is proposed. Person authentication results coming from several modalities (e.g. still image, speech), are combined by using fuzzy k-means (FKM) and fuzzy vector quantization (FVQ) algorithms, and median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a novel fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system.