Multimodal Biometrics for user authentication

Parkavi, R.; Babu, K. Ratna; Kumar, Jitendra

doi:10.1109/isco.2017.7856044

Cited by 27 publications

(16 citation statements)

References 7 publications

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“…There are some types of biometric systems that can be used, such as fingerprint, iris reader, facial recognition, and voice recognition. The use of multimodal biometrics has been considered in the study [44].…”

Section: Biometric Systemsmentioning

confidence: 99%

An Approach to Optimize the Management of Information Security in Public Organizations of Ecuador

Toapanta¹,

Gallegos²

2020

Fault Detection, Diagnosis and Prognosis

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The problems of information security in public organizations in Ecuador are evident, which, as a result, have led to corruptions that are present at all levels of operational, tactical and strategic management. The objective of this chapter is to analyze the available information found in different media, written, spoken, among others. The deductive method was used for the collection of information and observation techniques. It turned out the improve in the administrative processes, prototype diagram of sequence of access of users and services, prototype of integration of technologies of security of the information for public organizations of Ecuador. It was concluded that to avoid corruption in a country change should happen at all levels: the way of thinking and culture of the inhabitants, laws, penalties to politicians without parliamentary immunity, application of information and communications technologies (ICT) in an appropriate manner, and complying with international standards in information security. To improve information security, administrative policies on information security must be changed, and technologies related to immutable security algorithms, Ledger, Hyperledger, etc., must be used.

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Section: Biometric Systemsmentioning

confidence: 99%

An Approach to Optimize the Management of Information Security in Public Organizations of Ecuador

Toapanta¹,

Gallegos²

2020

Fault Detection, Diagnosis and Prognosis

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“…Since multimodal requires more than one biometric traits, face is the most common trait used to club with other biometric traits. Hong and Jain [5] where the first persons who proposed bimodal technique in the year 1998, they used [8]. In 2018 Waziha Kabir et al introduced hybrid fusion for authentication [9].…”

Section: Literature Surveymentioning

confidence: 99%

Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features

C¹,

Prakash²

2020

IJEAT

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Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for fingerprint. Hence, fusion of more than one biometric samples, traits or algorithms to achieve quality performance is another way to reap the better overall performance. In current scenario many researchers concentrating on Multimodal Biometrics with new fusion techniques ideas. We propose a new method of feature level fusion which uses Modified Adaptive Bilinear Interpolation (MABI) method to increase the resolution of data sample, which gives better features for fusion which gives more accurate results. In this work, experiment is done on AT&T face Cambridge University Computer Laboratory and MCYT signature Biometric Recognition Group datasets with combination of both unimodal and multimodal traits. K Nearest Neighbor (KNN) and Ensemble methods are used for classification. The proposed biometric system can be used in biometric surveillance, biometric screening for secured places, forensic applications etc.

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“…Podemos citar alguns exemplos de seu uso: caixas eletrônicos; smartphones, tablets e notebooks; controle ISSN: 1137-3601 (print), 1988-3064 (on-line) c IBERAMIA and the authors eletrônico de acesso aos locais de trabalho; identificação de usuários de planos de saúde. Em alguns casos, para aumentar ainda mais as taxas de correta identificação individual e o grau de confiabilidade no sistema que identificará o indivíduo, são utilizados sistemas biométricos multimodais, envolvendo mais de uma característica biométrica na identificação, como por exemplo, um sistema capturar a imagem dá ıris e da impressão digital e utilizar ambas para a identificação [3] [4][5] [6].…”

Section: Introductionunclassified

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Brito¹,

Ling²

2019

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This paper describes in detail different hand vein recognition methods based on Wavelet-SVM, Wavelet-ANN and Image Registration. A new image segmentation and processing algorithm is proposed to efficiently locate vein regions and suitable for feature extraction (wavelet coefficients and normalized vein imagens) and classification (SVM, ANN and Image Registration). For real time recognition and high recognition rate, we proposed an integrated system which combines three above mentioned classification methods. The simulation results reveal that the proposed integrated system achieves 1% false rejection rate (FRR) and 0.02% false acceptance rate (FAR). Resumo Este artigo descreve em detalhes diferentes métodos para o reconhecimento biométrico com base nas veias das mãos. Os métodos têm como base Wavelets com SVM, Wavelets com Rede Neural Artificial e Registro de Imagens. Um novo método de segmentação de imagensé proposto para localizar de modo adequado e eficiente a região das veias, permitindo a extração de características (com Wavelets ou a partir de uma imagem normalizada) e a classificação (com SVM, Rede Neural e Registro de Imagens). Para uma alta taxa de reconhecimento realizada em tempo real nós propomos um sistema híbrido que combina os três métodos de classificação mencionados. Os resultados de testes revelam que o sistema híbrido fornece uma taxa de falsa rejeição (FRR) de 1% para uma taxa de falsa aceitação (FAR) de 0.02%.

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Multimodal Biometrics for user authentication

Cited by 27 publications

References 7 publications

An Approach to Optimize the Management of Information Security in Public Organizations of Ecuador

An Approach to Optimize the Management of Information Security in Public Organizations of Ecuador

Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features

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