Automated Authentication Using Hybrid Biometric System

A highly reliable biometric authentication system can be realised by using multiple biometric models. In this study, a framework that makes use of signaland image-processing algorithms, together with pattern recognition techniques, is applied to solve the problem of biometric pattern recognition in a unified way. In general, this problem can be broken down into the following taxonomy: sensors, extractors, experts and the supervisor. Using this general schema, biometric systems with similar fundamental problem characteristics can be processed. According to the product law of reliability, a distributed (or parallel) system is more reliable than a linear system. Inspired by the idea of parallelism, ensemble methods and the notions of multi-sample and multimodel are studied. Based on the proposed framework, a hybrid biometric authentication prototype that makes use of upright frontal face-scans and text dependent voice-scans is implemented. This prototype has been tested on a real-life database in our laboratory with encouraging results. We show that multi-sample multi-model biometric approach is more reliable than other existing combination models (single-sample single-model, single-sample multi-model or multi-sample single-model). From the application point of