Thesis - Instituto de Telecomunicações

Chapter 5Feature Selection andClassificationThe present chapter introduces the set of classification tools to be used for biometric authenticationpurposes.We have developed statistical models for the features proposed in chapter 4. In orderto avoid dimensionality problems, we also apply feature selection techniques, which identifythe relevant features in terms of discriminative potential among the users. We define aclassification scheme that has the advantage of taking several samples from the same class,creating a sequential classifier. We also address the use of uncertainty information appliedto the classification process and to use it in classification fusion.5.0.1 NotationWe consider that the i th user is denoted by the class w i , i =1, . . . , n c , and n c is the numberof users. Given a sequence of n s consecutive patterns associated with a user, w i , we createthe matrix X =[x 1 , ··· , x ns ] , resulting from the feature vectors concatenation associatedwith each sample: x j =[x 1,j , ··· ,x nfi ,j] T ; the feature vector representing the jth sample,has n fj elements, n fj being the number of features identified for user w i during the featureselection phase.93