Large-scale biometric multimedia processing

Abstract

The field of Biometrics analyses organic signals from people to identify or verify an identity using a combination of physiological, behavioural or cognitive characteristics such as voice, fingerprints, eye color, facial features, iris, handwriting or other characteristics. Large-scale biometric identification systems can benefit from modern optimisation, classification and parallel computation techniques to reduce cost and increase accuracy. This chapter discusses recent and novel developments by the authors in the approaches taken to enable large-scale biometric identification. The authors present an overview of different techniques to perform the tasks of search space reduction, feature selection and parallel processing of biometrics data. Topics covered are: support vector machines and hyperspace transformations for effectively searching extremely large fingerprint databases to identify individuals; evolutionary computing to perform efficient facial feature selection for identification purposes; and cloud and high-performance designs for biometric systems.