Biometric dispersion matcher

Abstract

In this paper we propose a new classifier called a dispersion matcher. Our proposal is especially well adapted to those scenarios where a large number of classes and a small number of samples per class are available for training. This is the situation of biometric systems where just three to five measures per person are acquired during enrollment. This is just the opposite situation of other pattern recognition applications where a small number of classes and a large amount of training samples are available, such as handwritten digit recognition (10 classes) for ZIP code identification. The dispersion matcher trains a quadratic discriminant classifier to solve the dichotomy “Do these two feature vectors belong to the same person?”. In this way, we solve an important set of topics: (a) we can classify an open world problem and we do not need to train the model again if a new user is added, (b) we find a natural solution for feature selection, (c) experimental results with a priori threshold provides good results. We evaluate the proposed system with hand-geometry and face recognition problems (identification and verification). In hand geometry, we get a minimum detection cost function (DCF) for verification of 0.21% and a maximum identification rate of 99.1%, which compares favorably with other state-of-the-art methods. In face verification we achieve 5.59% DCF and 92.77% identification rate, which also compares favorably with the literature.