Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition

Abderrahmane Herbadji,Lahcene Ziet,Zahid Akhtar,Noubeil Guermat,Mohamed Cheniti,Kamran Siddique,Khan Muhammad

doi:10.3390/sym12030444

Abstract

Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast.

Highlights

Traditional authentication methods based on passwords and identity cards still face several challenges such as passwords can be forgotten or identity cards can be faked [1,2]
In order to alleviate some of the disadvantages of prior fusion algorithms as well as to increase the overall accuracy performance of multibiometric user authentication, in this work, we present a novel score level fusion method using Asymmetric Aggregation Operators (Asym-AOs), which are built via the generator functions of t-norms
Unlike previous multibiometric authentication systems that depend on samples collected using ordinary camera, we studied a multi-modal biometric framework based on datasets collected on mobile/smart phones, i.e., multi-modal system using face and ocular biometrics from Mobile Biometry (MOBIO) face and Visible Light Mobile Ocular Biometric (VISOB) iPhone Day Light Ocular biometric databases, respectively

Summary

Introduction

Traditional authentication methods based on passwords and identity cards still face several challenges such as passwords can be forgotten or identity cards can be faked [1,2]. Symmetry 2020, 12, 444 mechanisms due to the growth of threats in identity management and security tasks [3]. This technology is based on human anatomical (e.g., fingerprint, face, iris, palmprint) or behavioural (e.g., gait, signature, keystroke analysis) characteristics [2]. Unlike knowledge-based and token-based strategies for person recognition, biometric traits are able to guaranty that no user is able to assume more than one identity [2]. Due to above-mentioned reasons, biometrics recognition has been increasingly adopted in diverse fields such as airport checking, video surveillance, industries, commercial sectors, personal smartphone user authentication as well as forensics [4,5]

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