Score normalization refers to changing the location and scale parameters of the match score distributions at the outputs of the individual matchers, so that the match scores of different matchers are transformed into a common domain [ROS, 2006]. Integration at the match score level is generally preferred due to the presence of sufficient information content and the ease in accessing and combining match scores. The match score is a measure of similarity or dissimilarity between the input and template biometric feature vectors. Some issues need to be considered prior to combining the scores of the matchers into a single score. The matching scores at the output of the individual matchers may not be homogeneous. For example, one matcher may output a distance or dissimilarity measure (a smaller distance indicates a better match) while another may output a similarity measure (a larger similarity value indicates a better match). Further, the outputs of the individual matchers need not be on the same numerical scale(range).