Biometric information system is one of the ? nest examples of computer system that tries to imitate the decisions that humans make in their everyday life, speci? cally concerning people identi? cation and matching tasks. In this quest, the biometric systems evolved from simple single-feature-based models to a complex decision-making mechanism that utilize arti? cial intelligence, neural networks, complex decision making schemes, and multiple biometric parameters extracted and combined in an intelligent way.
The main goal and contribution of this Project is to present a comprehensive analysis of various biometric fusion techniques in combination with advanced biometric feature extraction mechanisms that improve the performance of the biometric information system in the challenging and not resolved problem of people identi? cation. A biometric identi? cation (matching) system is an automatic pattern recognition system that recognizes a person by determining the authenticity of a speci? c physiological and/or behavioral characteristic (biometric) possessed by that person.
Physiological biometric identi? ers include ? ngerprints, hand geometry, ear patterns, eye patterns (iris and retina), facial features, and other physical characteristics. Behavioral identi? ers include voice, signature, typing patterns, and others. In recent years, biometric authentication has seen considerable improvements in reliability and accuracy, with some biometrics offering reasonably good overall performance. However, even the most advanced biometric systems are still facing numerous problems, some inherent to the type of data and some to the methodology itself.
In particular, biometric authentication systems generally suffer from imprecision and dif? culties in person recognition due to noisy input data, limited degrees of freedom, intraclass variability, nonuniversality, and other factors that affect the performance, security, and convenience of using such systems. Multibiometrics is a relatively new approach to biometric knowledge representation that strives to overcome the problems by consolidating the evidence presented by multiple biometric traits/sources.
Multibiometric systems can signi? cantly improve the recognition performance in addition to improving population coverage, deterring spoof attacks, increasing the degrees of freedom, and reducing the failure-to-enroll rate. Although the storage requirements, processing time, and computational demands of a multibiometric system can be higher than that for a unimodal biometric system, the aforementioned advantages present a compelling case for deploying multibiometric systems in real-world large-scale authentication systems.
The key to successful multibiometric system is in an effective fusion scheme, which is necessary to combine the information presented by multiple domain experts. The goal of fusion is to determine the best set of experts in a given problem domain and devise an appropriate function that can optimally combine the decisions rendered by the individual experts. Pieces of evidence in a multibiometric system can be integrated in several different levels, but we can subdivide them in the following two main categories. Prior to matching fusion: Fusion in this category integrates pieces of evidence before matching.