A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

ElAlami, M. E.; Amin, Adnan; El-alfi, A. E.

doi:10.5120/8058-1411

Cited by 2 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In MultiBiometrics, biometric information is extracted from the multiple traits and sources (sensors, samples, backgrounds, algorithms, or/and units), and this information is manipulated, combined (or fused) for the exploitation of biometric encryption, enrollment, and authentication processes. The biometric system usually processes the biometric traits sequentially until the system achieves an acceptable recognition can overcome the challenges experienced by the monomodal biometric technique [28], [32][33][34].…”

Section: Biometric Modalitiesmentioning

confidence: 99%

Sequential subspace estimator for an efficient multibiometrics authentication and encryption

Malek¹

2023

Preprint

View full text Add to dashboard Cite

The principal challenge in biometric authentication is to mitigate the effects of any noise while extracting biometric features for biometric template generation. Most biometric systems are developed under the assumption that the extracted biometrics and the nature of their associated interferences are linear, stationary, and homogeneous. When these assumptions are violated due to nonlinear, nonstationary, and heterogeneous noise, the authentication performance deteriorates. As well, demands for biometric templates are on the rise in the field of information technology, leading to an increase in the vulnerability of stored and dynamic information. Thus, the development of a sophisticated authentication and encryption method is necessary to address these challenges. This dissertation proposes a new Sequential Subspace Estimator (SSE) algorithm for biometric authentication. In the proposed method, a sequential estimator is being designed in the image subspace that addresses challenges arising from nonlinear, nonstationary, and heterogeneous noise. The proposed method includes a subspace technique that overcomes the computational complexity associated with the sequential estimator. In addition, it includes a novel MultiBiometrics encryption algorithm that protects the biometric templates against security, privacy, and unlinkability attacks. Unlike current biometric encryption, this method uses cryptographic keys in conjunction with extracted MultiBiometrics to create cryptographic bonds, called “BioCryptoBond”. To further enhance system security and improve authentication accuracy, the development of a biometric database management system is also being considered. The proposed method is being tested on images from three public databases: the “Put Face Database”, the “Indian Face Database”, and the “CASIA Fingerprint Image Database Version 5.1”. The performance of the proposed solution has been evaluated using the Equal Error Rate (EER) and Correct Recognition Rate (CRR). The experimental results demonstrate the superiority of the proposed method in comparison to its counterparts.

show abstract

Section: Biometric Modalitiesmentioning

confidence: 99%