Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

Damousis, I. G.; Argyropoulos, Savvas

doi:10.1155/2012/242401

Cited by 24 publications

(12 citation statements)

References 7 publications

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“…These algorithms have been applied in a wide spectrum of science and engineering problems, including individual human recognition through biometric data15 and identification of trends in climate and weather16. Among these methods, a selection works by embedding a data set on a manifold and studying its structure as a proxy for the more complex data set.…”

mentioning

confidence: 99%

“…Recently, a variety of machine learning algorithms, such as support vector machines 11 , local linear embedding 12 , and principal component analysis 13 , have been developed to extract patterns from high-dimensional data sets 14 . These algorithms have been applied in a wide spectrum of science and engineering problems, including individual human recognition through biometric data 15 and identification of trends in climate and weather 16 . Among these methods, a selection works by embedding a data set on a manifold and studying its structure as a proxy for the more complex data set.…”

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confidence: 99%

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Collective behaviour across animal species

DeLellis

Polverino

Ustuner

et al. 2014

Sci Rep

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We posit a new geometric perspective to define, detect, and classify inherent patterns of collective behaviour across a variety of animal species. We show that machine learning techniques, and specifically the isometric mapping algorithm, allow the identification and interpretation of different types of collective behaviour in five social animal species. These results offer a first glimpse at the transformative potential of machine learning for ethology, similar to its impact on robotics, where it enabled robots to recognize objects and navigate the environment.

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confidence: 99%

mentioning

confidence: 99%

Collective behaviour across animal species

DeLellis

Polverino

Ustuner

et al. 2014

Sci Rep

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“…While unsupervised techniques are used for discovering clusters, discovering latent factors, discovering graph structure, matrix completion, supervised learning is focused on classification and regression. It has been proofing supervised learning is useful for biometric modalities fusion [60], biometric data classification [61], [62] and regression for reliable, successful and secure multibiometric systems [63], [64]. Interesting results have been obtained from modern techniques.…”

Section: Supervised Learningmentioning

confidence: 99%

Survey of biometric pattern recognition via machine learning techniques

Ortiz¹,

Hernández²,

Jimenez³

et al. 2018

ces

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Biometrics, as a computer science field, can be understood as the discipline that study how to generate computer models of the physical (e.g. hand geometry, fingerprints, iris and so on) and behavioral (e.g. signature; a kind of behavior pattern) characteristics of the human being for single or several individuals identification. Usually, these characteristics are used to provide authentication information for security systems. However, some of these characteristics are hard to obtain in a properly way and it is necessary to use several algorithms both to process them and to use them on a security systems. In this sense, in this paper it is presented an overview of some Machine Learning approaches for biometric pattern recognition.

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“…[21]; [7]. Hence, the need to combine more than one biometric trait in order to ensure secured, acceptable, effective, reliable and efficient e-payment systems.…”

Section: Figure 1: Typical Biometric Enrollment and Recognition Procementioning

confidence: 99%

Enhanced Biometric Authentication System for Efficient and Reliable e-Payment System in Nigeria

Aranuwa¹,

Ogunniye²

2012

IJAIS

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The recently introduced cashless economy with the cash-lite banking by the Central Bank of Nigeria (CBN) has engineered most Nigeria banks to introduce e-payment and e-transact solutions to support the policy. However, one of the major problems limiting the growth of this new move in Nigeria is the absence of secure and reliable e-payment systems. The problems associated with the implementation of a secure e-payment systems in the country stem from card thefts, internet fraud and identity theft e.t.c, which runs into millions of US dollars annually. This has adversely affected the integrity, development of e-commerce and the country's active participation in the international market. Hence, the need for a secured and reliable mechanism for a proficient implementation of e-payment system in the country. This paper is focused on securing reliable authentication scheme for e-payment system in Nigeria through effective biometric authentication technology.

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Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

Cited by 24 publications

References 7 publications

Collective behaviour across animal species

Collective behaviour across animal species

Survey of biometric pattern recognition via machine learning techniques

Enhanced Biometric Authentication System for Efficient and Reliable e-Payment System in Nigeria

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