Biometrics-as-a-service: A framework to promote innovative biometric recognition in the cloud

Talreja, Veeru; Ferrett, Terry; Valenti, Matthew C.; Ross, Arun

doi:10.1109/icce.2018.8326075

Cited by 25 publications

(16 citation statements)

References 18 publications

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“…There is also evidence that access control to documents requiring security clearance for defense, healthcare, and financial records can be conveniently provided using biometric authentication service [35]. BaaS can provide benefits for biometric user authentication such as a ubiquitous service, virtually unlimited hardware, and storage resources at an economical price [2] [22]. Based on the analysis and prediction of the market demands in the near future, BaaS can be used for biometric identification in law enforcement, forensics, and criminal identification by utilizing public biometric repositories on the cloud [34].…”

Section: Literature Reviewmentioning

confidence: 99%

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Cancelable Biometrics Using Deep Learning as a Cloud Service

Sudhakar

Gavrilova

2020

IEEE Access

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Cloud computing is a technology that has gained rapid popularity in recent years. It has enabled use of immense computational power in a scalable and cost-efficient manner. Deployment of biometric technology in government and commercial organizations has become a standard security practice. However, independent biometric systems tend to be computationally and financially expensive, especially when user enrollment is high. A feasible solution is to create a biometric system on the cloud which can be used ubiquitously as an authentication service. In this paper, we propose a first cancelable biometric framework based on deep learning on the cloud. We establish that cloud is a good solution for biometric systems where intensive computation, quick response times, and high accuracy is required. INDEX TERMS Biometric privacy, cloud computing, cancelable biometrics, deep learning.

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Section: Literature Reviewmentioning

confidence: 99%

“…Parallel computation using multiple servers and nodes dis-tributes processing, thereby reducing response time. Thus, Biometrics-as-a-Service (BaaS) or cloud based biometric authentication emerged as a high-impact research area [2] [22].…”

Section: Introductionmentioning

confidence: 99%

Cancelable Biometrics Using Deep Learning as a Cloud Service

Sudhakar

Gavrilova

2020

IEEE Access

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“…As the very recent studies mention, there has been a tremendous effort to optimise solutions that perform biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smart phones [1, 8]. Since cloud platforms present scalable and affordable storage capacity and computation power, cloud computing may be utilised to meet the storage and computation needs of biometric systems by out‐sourcing the complex tasks to infrastructures [19].…”

Section: Survey Of Existing Studiesmentioning

confidence: 99%

“…Whatever the field of application considered, the biometric systems rely on distributed networks where remote authentication, remote analysis, or tracking are conducted. Within these distributed networks, secure transaction of too much data has become an issue where biometric services contribute to the creation of big data over networks [8]. The use of BaaS in coordination with other layers of cloud environments, such as infrastructure‐as‐a‐service (IaaS), platform‐as‐a‐service (PaaS), and software‐as‐a‐service (SaaS), has become forward to provide effective and parallel use of biometric services at different locations and times.…”

Section: Introductionmentioning

confidence: 99%

Biometric ontology for semantic biometric‐as‐a‐service (BaaS) applications: a border security use case

Kanak¹

2018

IET biom.

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With the fast adoption of cloud computing, the use of biometric technologies has evolved to a different way of providing security, preserving privacy, and analysing personal traits for various purposes. The main components of any biometric system, such as biometric sensing, data gathering, feature extraction, identification, verification, recognition, and analytics, are now handled over distributed networks. Many of the biometric system services are presented over such networks which are followed by the creation of a new concept 'biometric-as-a-service (BaaS)'. Recent BaaS approaches usually focus on identifying the effective distributed architectures, policies, and use case recommendations. However, there is a strong need to focus on developing a semantic framework which should rely on a biometric ontology. This study presents such an ontology covering the uses of different biometric modalities, evaluation and assessment of biometric systems, modelling biometric processes, and analyses through interlinked relations with biometric stakeholders. In order to shed light on how such an ontology is useful for BaaS solutions, a case study focusing on the various uses of biometric modalities is presented. The selected use case addresses the asylum seeker or immigrant identification problems regarding the border security challenges where facial biometrics are benefited.

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“…В последнее время биометрия -наука о распознавании личности по ее физическим и поведенческим признакам -стремительно развивалась, а ее достижения нашли применение в ряде областей, таких как правоохранительные, медицинские и финансовые службы [1]. Основная задача биометрической системы заключается в сравнении двух биометрических экземпляров и установлении принадлежности их к одной или разным личностям.…”

Section: Introductionunclassified

Two-component network model in voice identification technologies

Kuular

Trufanov

Tikhomirov

2018

BIT

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Аннотация. Среди важнейших параметров биометрических систем голосовой модальности, определяющих их эффективность, наряду с надежностью и помехоустойчивостью выделяют скорость идентификации и верификации личности. Данный параметр особенно чувствителен при обработке крупномасштабных баз голосовых данных в режиме реального времени. Многие исследовательские работы в данной области направлены на разработку новых и совершенствование имеющихся алгоритмов представления и обработки речевых записей, обеспечивающих высокую производительность голосовых биометрических систем. Здесь перспективным представляется современный подход к решению сложных объемных задач с большим числом элементов и учетом их взаимосвязей, использующий платформу комплексных сетей. Так, известны работы, в которых при решении задачах анализа и распознавания лиц по фотографиям изображения трансформируются в комплексные сети для последующей их обработки стандартными приемами. Одним из первых применений комплексных сетей в задачах анализа звуковых рядов (музыкальных и речевых) являлось описание их частотных характеристик с помощью построения сетевых моделей -конвертации рядов в сети. На платформе сетевой онтологии в данной работе развит предложенный ранее метод представления аудиоинформации с целью ее высокопроизводительного автоматического анализа и снижения погрешности в распознавании личности -диктора. Для этого предполагается конвертирование звуковой информации в форму ассоциативной семантической (когнитивной) сетевой структуры, содержащей две компоненты: амплитудную и частотную. Были записаны два речевых примера с последующей их трансформацией в соответствующие сети и сравнением топологических метрик. Набор топологических метрик каждой из сетевых моделей (амплитудной и частотной) представляет собой вектор, а вместе -матрицу, как цифровой "сетевой" отпечаток голоса. Предлагаемый сетевой подход с его чувствительностью к состоянию индивида -физиологическому, психологическому, эмоциональному, может оказаться полезным не только для задач идентификации личности, но и для определения ее состояния.

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Biometrics-as-a-service: A framework to promote innovative biometric recognition in the cloud

Cited by 25 publications

References 18 publications

Cancelable Biometrics Using Deep Learning as a Cloud Service

Cancelable Biometrics Using Deep Learning as a Cloud Service

Biometric ontology for semantic biometric‐as‐a‐service (BaaS) applications: a border security use case

Two-component network model in voice identification technologies

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