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

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.

An Approach for Full Reinforcement-Based Biometric Score Fusion

“…In their evaluation of multimodal, multi-unit, and multi-algorithm systems, they demonstrated that the WQAM combining rule they proposed surpasses the earlier suggested score combining method, which relied on fixed techniques like T-norms, trained methods such as support vector machines, and density assessment approaches like likelihood ratio. [11] 2013 Sum, max, min rule iris and fingerprint [34] 2013 Mean, max, min, median rule Palm-print-vein [13] 2014 Mean rule face and fingerprint [14] 2015 Max rule palm-dorsal vein [12] 2016 Hanman-Anirban entropy function finger-knuckle-print (left index, left middle, right index and right middle) [33] 2017 Adaptive weighted Left and right palmprint [15] 2017 Dubois and Parad T-norm left and right wrist vein patterns [7] 2018 Symmetric sum rule face and fingerprint [16] 2019 T-norm wrist, palm vein images [8] 2020 Weighted quasiarithmetic mean face and fingerprint [9] 2020 Asym-AO palmprint, fingerprint, face and ocular left eye…”

“…By combining evidence from multiple biometric sources, it becomes significantly more challenging for impostors to simultaneously imitate various physiological and/or behavioral attributes of a genuine user [7]. According to the nature of the biometric information source, a multibiometric recognition can be categorized broadly into several types, such as multimodal (utilizing multiple biometric traits) [8], multiunit (employing multiple instances of the same kind of biometric data, e.g., both left and right wrist veins in humans) [8], multi-algorithm (applying diverse feature extraction methods on the identical biometric characteristic) [8], multisensor (utilizing multiple sensors for capturing the identical biometric trait) [9], and multi-sample (gathering a multitude of samples of the identical biometric characteristic) [9]. To address the limitations observed in prior research on both unimodal and multimodal biometrics, it is imperative to develop contemporary and computationally efficient methods for combining authentication scores.…”

Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

“…In order to attain a reliable solution for person authentication or identification, multi-biometric systems have been proposed. Diverse set of studies proved the effectiveness of multi-biometrics authentication systems [17][18][19], and showed that in multi-biometric systems different biometric cues compensate inherent frailties of other uni-modal biometric systems. Although biometric features can be fused at different levels (i.e., feature, scores, decision), the fusion of match-scores is considered convenient, because while it can boost the system's reliability, it decreases overall complexity.…”

“…Although biometric features can be fused at different levels (i.e., feature, scores, decision), the fusion of match-scores is considered convenient, because while it can boost the system's reliability, it decreases overall complexity. Researchers have presented numerous techniques to integrate match-scores [19][20][21][22][23]. For instance, Peng et al [24] used tnorms to fuse multiple finger traits such as fingerprint, finger vein, finger knuckle print, and finger shape.…”

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

“…Studies show that the groove pattern and even the hand arteries can be unique and identify individuals with great accuracy. This method can use tissue features such as wrinkles, bumps, and the human palm folds for identification [12][13].…”