Template security analysis of multimodal biometric frameworks based on fingerprint and hand geometry

Selwal, Arvind; Gupta, Sunil Kumar; Surender,; Anubhuti,

doi:10.1016/j.pisc.2016.06.065

Cited by 22 publications

(13 citation statements)

References 5 publications

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“…at sensor level and is expected to produce more accurate results than the system that integrate information at later stages due to the availability of more information [4][5], . In 2003, Liu and Chen [1], propose a face mosaic king technique.…”

Section: Literature Surveymentioning

confidence: 99%

“…Multibiometric systems that have been proposed in the literature can be categorized based on three parameters, i.e., (i) type of biometric information fused, (ii) level of fusion, and (iii) fusion methodology. In general, the choice of these three parameters depends on the application scenario and it has been found that they have a profound influence on the performance of a multibiometric system [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of multiple fusion approaches for multimodal biometric systems

2017

IJLTET

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative analysis of multiple fusion approaches for multimodal biometric systems

2017

IJLTET

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“…These methods are more reliable and capable than knowledge-based techniques [17] - [24]. Since biometric features are hardly stolen or forgotten.…”

Section: Introductionmentioning

confidence: 99%

“…By combining multiple modalities enhanced performance reliability could be achieved. Measurable means that the characteristic or trait can be easilypresented to a sensor and converted into a quantifiable,digital format [17] - [24]. This allows for the automated matchingprocess to occur in a matter of seconds.The robustness of a biometric is a measure of the extent towhich the characteristic or trait is subject to significantchanges over time.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Biometric Identification using Face and Palmprint

Jain¹,

Jangra²,

Kaur³

2017

IJARCSSE

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Biometrics recognizes individual basedon the features from their physiological and/or behavioural characteristics. These systems provide reliable recognition schemes for determining the individual identity. Applications of these systems include computer systems security, credit card, access to buildings in a secure way. Using biometric, the person itself is a password. Fusion at the feature level is believed to give better results by incorporating feature vectors which contain richer information which is nonlinear and captures different statistical properties. Therefore, a new feature extraction framework is proposed here from the fusion of face and palmprint biometric. The proposed approach focuses on the development of this new framework for the fusion purpose. This method is able to extract important information in different orientations and scales, and is thus able to capture nonlinear information in different biometric image like face and palmprint etc. The proposed paper shows that integration of face and palmprint biometrics can achieve higher performance that may not be possible using a single biometric indicator alone.Keywords: Biometrics, Multimodal Biometrics, Identification, Security, Verification, Templates, Fusion, Local Feature Extraction.. I. INTRODUCTIONA multimodal biometric authentication recognizes an individual person using physiological and/or behavioral characteristics, such as finger knuckle print, face, fingerprints, hand geometry, iris, retina, vein and speech is one of the most attractive and effective methods. These methods are more reliable and capable than knowledge-based techniques [17] - [24]. Since biometric features are hardly stolen or forgotten. However, a single biometric feature sometimes fails to be exact enough for verifying the identity of a person. By combining multiple modalities enhanced performance reliability could be achieved. Measurable means that the characteristic or trait can be easilypresented to a sensor and converted into a quantifiable,digital format [17] -[24]. This allows for the automated matchingprocess to occur in a matter of seconds.The robustness of a biometric is a measure of the extent towhich the characteristic or trait is subject to significantchanges over time. These changes can occur as a result ofage, injury, illness, occupational use, or chemical exposure.A highly robust biometric does not change significantly overtime. A less robust biometric does. For example, the iris,which changes very little over a person"s lifetime, is morerobust than a voice. Due to its promising applications as well as the theoretical challenges, multimodal biometric has drawn more and more attention in recent years [1]. Face and palmprint multimodal biometrics are advantageous due to the use of non-invasive and low-cost image acquisition. In this method we can easily acquire face and palmprint images using two touchless sensors simultaneously. Existing studies in this approach [2,3] employ holistic features for face representation and results are shown with ...

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“…In biometrics, single modal techniques (methods that use a single type of bio-information to identify individuals) have been prevalent so far; however, multimodal techniques (methods that use multiple types of bio-information to identify individuals) have become common in recent years in order to identify a person with high accuracy and strengthen the robustness of the technique to impersonation attacks. In fact, a technical report on multimodal identification systems was published by ISO [1], and in India, faces, fingerprints, and iris information are  This work was supported by the Kurata Memorial Hitach Science and Technology Foundation and the JSPS KAKENHI Grant Number JP16H07178 used to identify individuals in the national identification number project [2].…”

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confidence: 99%

Multimodal Artifact Metrics for Valuable Resin Card

Fujikawa

Jitsukawa

Fuchi

2017

Annals of Computer Science and Information Systems

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Abstract-This study focuses on multimodal artifact metrics and proposes a technique based on multimodal biometric systems that are a type of biometric identification systems. It is expected that this technique can aid in verifying the authenticity of each artifact in a more accurate manner and in increasing the level of difficulty involved in counterfeiting when compared to those of existing artifact metric techniques. This technique will ensure that artifacts will possess specific characteristics (two or more) that are extracted from different physical characteristics. The study created card-shaped samples with two physical characteristics (electrical and optical characteristics) to prove the feasibility of the proposed technique. The results indicate that two information features, namely sheet resistance and visible light image, which are extracted from the above characteristics, are different in each sample. This indicates that the two information features can correspond to the characteristic information necessary to distinguish each sample.

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Template security analysis of multimodal biometric frameworks based on fingerprint and hand geometry

Cited by 22 publications

References 5 publications

Comparative analysis of multiple fusion approaches for multimodal biometric systems

Comparative analysis of multiple fusion approaches for multimodal biometric systems

Multimodal Biometric Identification using Face and Palmprint

Multimodal Artifact Metrics for Valuable Resin Card

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