Multimodal Biometric System Using Rank-Level Fusion Approach

Monwar, Md. Maruf; Gavrilova, Marina L.

doi:10.1109/tsmcb.2008.2009071

Cited by 178 publications

(90 citation statements)

References 24 publications

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“…However, it is better to use another biometrics to reduce these variations. By choosing multimodal biometrics, there will be an increase in population coverage, in particular, if one biometric has lower universality than the other [2]. Moreover, some single biometric systems have relatively high unacceptable error rates, and that can be mitigated by using the multimodal systems too.…”

Section: Unimodal Versus Multimodal Biometrics Systemsmentioning

confidence: 99%

“…However, some issues should be taken into account when considering multimodal biometrics systems. Multimodal system storage requirements are higher compared to the unimodal systems as there are more than one biometrics traits to store, processing and computational times also expected to be longer [2]. Also, one of the important considerations either in unimodal or multimodal systems are the ethical and privacy issues.…”

Section: Unimodal Versus Multimodal Biometrics Systemsmentioning

confidence: 99%

“…Physiological and behavioral human biometrics should answer this question. As illustrated in Fig.1 bellow, the biometrics systems -in general-have four main components: (1) sensor (2) feature extraction (3) matching and (4) database. Sensors play the role of the user interface to collect raw biometric data from users, then the features of biometric traits are extracted from the raw data and stored in the databases as well as other personal information (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Voice & Ear Biometrics Authentication System

Mohammed¹,

Hegarty²

2017

IJEME

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The core aim of biometrics authentication methods and technologies is to solve issues and concerns existing in traditional authentication methods like passwords, PIN numbers or identity cards. The Most important concern for business sectors and organizations is to authenticate individuals who interact with them and their services. By considering more than one biometric technology, the authentication process is expected to be more reliable and secure. The Ear and Voice Multimodal Biometrics System is evaluated to compare its performance with ear and voice unimodal systems; the multimodal system takes advantages of the permanence characteristic of ear biometric and voice biometric which is highly acceptable by users. According to the experiment, the ear and voice multimodal system provides better performance than the ear or voice unimodal system. In addition to that, the multimodal system makes a right balance between false rejection and false acceptance rates. This evaluation is intended to contribute to multimodal biometrics research by using behavioral biometric (voice print) and physiological biometric (ear-print) and makes advantage of using both of them in one system.

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Section: Unimodal Versus Multimodal Biometrics Systemsmentioning

confidence: 99%

Section: Unimodal Versus Multimodal Biometrics Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Voice & Ear Biometrics Authentication System

Mohammed¹,

Hegarty²

2017

IJEME

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“…Different biometrics or modalities provide the system with more information about the users. A multimodal biometric system benefits from this extra information to increase its precision and is expected to have a higher identification rate than unimodal systems based on each of the modalities [8]. The use of a proper fusion technique is essential in consolidating multimodal information.…”

Section: Biometric Systemsmentioning

confidence: 99%

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Talebi

Gavrilova

2015

Computer Analysis of Images and Patterns

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In recent years, the inevitable need for reliable biometric identity management systems in applications such as border crossing, welfare distribution, and accessing critical facilities has drawn researchers' attention to the area of biometric. The intrinsic limitations of unimodal biometric systems such as non-universality, sensitivity to noisy sensor data, inter and intra class variations and spoof attacks have resulted in significant attention toward multimodal biometric systems. An important aspect of a multimodal biometric system is the fusion of information from multiple biometric sources. This thesis focuses on using the notion of Resemblance Probability Distributions to calculate confidence measures for different biometric matchers and use these confidence measures in the fusion module to improve the identification rate of the system. This thesis approaches the problem of low inter class variation and low quality data by proposing Rank List Reinforcement and Confidence-based Ranked List Selection methods.

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“…Adaptive rank level fusion: An effective adaptive rank level fusion scheme that combine information presented by multiple domain experts based on the rank-level fusion integration method (Monwar and Gavrilova, 2009). …”

Section: Verfication and Rank-level Fusion: Fingerprint Verificationmentioning

confidence: 99%

Efficient Multimodal Biometric Authentication Using Fast Fingerprint Verification and Enhanced Iris Features

Basha¹

2011

Journal of Computer Science

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Problem statement:The accuracy of biometric systems varies with the kind of biometric feature used in it. The Unmoral biometric system is prone to interclass variations. Approach: We implement Multimodal biometric systems to overcome the limitations by using multiple pieces of evidence of the same identity. However, the multimodal biometric system is limited to the time constraints due to its multiple processing stages. To improve the speed of authentication in the biometric system with acceptable accuracy, we have introduced a dynamic fingerprint verification technique fused with enhanced iris recognition using the adaptive rank level fusion method. Results: When tested upon the standard biometric dataset the system shows improvement in the False Acceptance Rate (FAR) and Equal Error Rate (EER) curves. Essentially, the time taken for the training and verification phase has a reduction of 10% when compared with the existing systems. Conclusion:The multimodel system has necessarily increased the speed and performance of the verification system especially when tested on slow processing and low memory devices.

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Multimodal Biometric System Using Rank-Level Fusion Approach

Cited by 178 publications

References 24 publications

Evaluation of Voice & Ear Biometrics Authentication System

Evaluation of Voice & Ear Biometrics Authentication System

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Efficient Multimodal Biometric Authentication Using Fast Fingerprint Verification and Enhanced Iris Features

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