Applying Bayes based classifiers for decision fusion in a multi-modal identity verification system

The contribution of this paper is threefold: (1) to formulate a decision fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose a general Bayes based classifier framework to solve this problem, (3) to derive and to compare two specific particularizations from this general framework. The multi-modal identity verification system under consideration is built of n modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A fusion module receiving as input the n scores has to take a binary decision: acceptor rejectidentity. We have solved this fusion problem using Bayes based classifiers applied in two specific cases: one using Gaussian distributions and another one using distributions from the exponential family with equal dispersion parameters, which leads to the logistic regression model. The effect of the a priori probabilities is highlighted in this context. The performances of these two fusion modules have been evaluated and compared with other approaches on a multimodal database, containing both vocal and visual modali-

[1]  Pierre A. Devijver.  Pattern recognition , 1982 .

[2]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[3]  G. Chollet,et al.  Evaluating speech recognizers and data bases , 1988 .

[4]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[5]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[6]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[7]  Belur V. Dasarathy,et al.  Decision fusion , 1994 .

[8]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[9]  Michael I. Jordan.  Why the logistic function? A tutorial discussion on probabilities and neural networks , 1995 .

[10]  F. Bimbot,et al.  Second-order statistical measures for text-independent speaker identification , 1995, Speech Commun..

[11]  Gérard Chollet,et al.  Assessment of speaker verification systems , 1995 .

[12]  Robert P. W. Duin,et al.  A note on comparing classifiers , 1996, Pattern Recognit. Lett..

[13]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[14]  George B. French,et al.  Bayesian Analysis in Practice , 1997 .

[15]  Gérard Chollet,et al.  Combining vocal and visual cues in an identity verification system using K-NN based classifiers , 1998, 1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175).

[16]  Jiri Matas,et al.  Fast face localisation and verification , 1999, Image Vis. Comput..

[17]  Gérard Chollet,et al.  Comparing decision fusion paradigms using -NN based classifiers, decision trees and logistic regression in a multi-modal identity verification ap plication , 1999 .

[18]  J. van Leeuwen,et al.  Audio- and Video-Based Biometric Person Authentication , 2001, Lecture Notes in Computer Science.