Biometric Authentication - A Machine Learning Approach

Preface

Biometrics has long been an active research field, particularly because of all the attention focused on public and private security systems in recent years. Advances in digital computers, software technologies, and embedded systems have further catalyzed increased interest in commercially available biometric application systems. Biometric authentication can be regarded as a special technical area in the field of pattern classification. Research and development on biometric authentication have focused on two separate fronts: one covering the theoretical aspect of machine learning for pattern classification and the other covering system design and deployment issues of biometric systems. This book is meant to bridge the gap between these two fronts, with a special emphasis on the promising roles of modern machine learning and neural network techniques.

To develop an effective biometric authentication system, it is vital to acquire a thorough understanding of the input feature space, then develop proper mapping of such feature space onto the expert space and eventually onto the output classification space. Unlike the conventional template matching approach, in which learning amounts to storing representative example patterns of a class, the machine learning approach adopts representative statistical models to capture the characteristics of patterns in the feature domain.
This book explores the rich synergy between various machine learning models from the perspective of biometric applications. Practically, the machine learning models can be adopted to construct a robust information processing system for biometric authentication and data fusion. It is potentially useful in a broad spectrum of application domains, including but not limited to biometric authentication.