fourth component is of great importance: the feature-extraction algorithm. The feature-extraction algorithm produces a feature vector, in which the components are numerical characterizations of the underlying biometrics. The feature vectors are designed to characterize the underlying biometrics so that biometric data collected from one individual at different times are similar, while those collected from different individuals are dissimilar. In general, the larger the size of a feature vector (without much redundancy), the higher its discrimination power. The discrimination power is the difference between a pair of feature vectors representing two different individuals. The fifth component of the system is the “matcher,” which compares feature vectors obtained from the feature extraction algorithm to produce a similarity score. This score indicates the degree of similarity between a pair of biometrics data under consideration. The sixth component of the system is a decision maker.