Extended Metacognitive Neuro-Fuzzy Inference System for Biometric Identification

Abstract

Biometrics are increasingly being used as security measures in online as well as offline systems, giving rise to more reliable and unique authentication techniques. In these systems, false positive minimization is one of the crucial requirements, which is especially critical in security sensitive applications. In this chapter, we present an Extended Metacognitive Neuro-Fuzzy Inference System (eMcFIS) based biometric identification system. eMcFIS consists of a cognitive component and a metacognitive component. The cognitive component, which is a neuro-fuzzy inference system, learns the input-output relationship efficiently. The metacognitive component is a self-regulatory learning mechanism, which actively regulates the learning in the cognitive component such that the network avoids over-fitting the training samples. Further, the learning strategies are chosen such that the network minimizes false-positive prediction. The proposed eMcFIS is first benchmarked on a set of medical datasets from machine learning databases. eMcFIS is then employed in detection of two real-world biometric security applications, signature verification and fingerprint recognition. The performance comparison with other state-of-the-art authentication systems clearly highlights the advantages of the proposed approach.