Remote multimodal biometric identification based on the fusion of the iris and the fingerprint

Aizi, Kamel; Ouslim, Mohamed; Sabri, Ahmed

doi:10.1109/intee.2015.7416673

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The achievement conferred mosaiced templates choose outperform personal fingerprint pictures. Aizi, and Kamel et al [15] and M. Haghighat, a proposed combining one texturebased -matched-filter) and 3minutiae based namely Houghtransform, 1D string based and 2D dynamic programming based, at decision level in an optimum way. Their stresses were on classifier collection appear to last combination.…”

Section: \ Fig1 Block Diagram For Multimodal Fusion Methodsmentioning

confidence: 99%

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Vijayakumar.¹,

K²

2019

IJRTE

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Abstract: A fusion scheme in multimodal biometric system is proposed in the name of Multimodal Feature Template Matching (MTM) system derived from the two picture fused values of average point and weighted assessment point. The main objective of MTM algorithm is to design the average point fusion and weighted average point by fusion of Iris and fingerprint features. The MTM algorithm is assigned with avgweights based on their verification accuracy. A hybrid approach of combining biometric image level information id presented. The raw information is merged at image level. This integration addressed, provides a small template and resilience to attacks. However it does not improve the recognition performance, but the performances is also less combination to its unimodal counterpart. Confidence level integration of compatible features of two different uncorrelated biometric traits fingerprint and iris provides sustainable improvement in performance accuracy compared to other integration methods as well as best unimodal system. Fingerprint and iris integrationapproaches presented in this proposed are more robust and reliable.

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Section: \ Fig1 Block Diagram For Multimodal Fusion Methodsmentioning

confidence: 99%

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Vijayakumar.¹,

K²

2019

IJRTE

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“…Another flavor of previous work includes studies [1,11] reviewing the feasibility of using multimodal biometrics to achieve enhanced identification performance. The obtained results from these studies indicate that, for small end-user populations, it is indeed possible.…”

Section: Related Workmentioning

confidence: 99%

Metadata filtering for user-friendly centralized biometric authentication

Gehrmann

Rodan

Jonsson

2019

EURASIP J. on Info. Security

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While biometric authentication for commercial use so far mainly has been used for local device unlock use cases, there are great opportunities for using it also for central authentication such as for remote login. However, many current biometric sensors like for instance mobile fingerprint sensors have too large false acceptance rate (FAR) not allowing them, for security reasons, to be used in larger user group for central identification purposes. A straightforward way to avoid this FAR problem is to either request a user unique identifier such as a device identifier or require the user to enter a unique user ID prior to making the biometric matching. Usage of a device identifier does not work when a user desires to authenticate on a previously unused device of a generic type. Furthermore, requiring the user at each login occasion to enter a unique user ID, is not at all user-friendly. To avoid this problem, we in this paper investigate an alternative, most user-friendly approach, for identification in combination with biometric-based authentication using metadata filtering. An evaluation of the adopted approach is carried out using realistic simulations of the Swedish population to assess the feasibility of the proposed system. The results show that metadata filtering in combination with traditional biometric-based matching is indeed a powerful tool for providing reliable, and user-friendly, central authentication services for large user groups.

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“…[7] Essentially orientation-sensitive, these are used for texture analysis. The Gabor wavelet coefficients are quantised to result in a binary image of the iris code.…”

Section: Filtersmentioning

confidence: 99%

“…This is done in order to achieve the smallest possible combined score. [7] A sample decision tree for iris and fingerprint recognition, where ISi is iris score, and FSi is fingerprint score, is as in Fig. 4.…”

Section: Identificationmentioning

confidence: 99%

Multimodal Biometric Systems: A Review

Thadeshwar

Patel

et al. 2018

ijarcs

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Abstract:The need for biometrics is rising with an increased need for security in every organization of the world. Every attribute, fingerprint, iris, knuckle print, face, palm print, is unique to an individual and can aid in recognition. Unimodal biometric systems involve the use of a single biometric attribute for identification. However, with the rise of duping, there is a requirement for constructing an efficient system using multiple biometric attributes. In multimodal systems, fusion of various biometric identities into a single vector is done using algorithms. This paper covers several different techniques used for biometric authentication. These use varying numbers of attributes. Our future project focuses on the research of different techniques of multimodal recognition in order to devise a technique that is robust and has an increased efficiency in recognition and identification.

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Remote multimodal biometric identification based on the fusion of the iris and the fingerprint

Cited by 6 publications

References 29 publications

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Robust Iris and Fingerprint Biometric Fusion in Multimodal Feature Template Matching

Metadata filtering for user-friendly centralized biometric authentication

Multimodal Biometric Systems: A Review

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