The Fall of One, the Rise of Many: A Survey on Multi-Biometric Fusion Methods

Dinca, Lavinia Mihaela; Hancke, Gerhard P.

doi:10.1109/access.2017.2694050

Cited by 68 publications

(25 citation statements)

References 264 publications

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“…The interested reader is therefore referred to e.g. Ross et al [32] for a comprehensive general introduction to this topic, Jain et al [39] and Snelick et al [38] for score-level fusion specifically, as well as Radu et al [44], Dinca et al [45], and ISO/IEC TR 24722 [33] for more recent works concerning the overall topic of biometric information fusion.…”

Section: Biometric Information Fusionmentioning

confidence: 99%

Multi-Biometric Identification With Cascading Database Filtering

Drozdowski

Rathgeb

Mokros

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

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The growing scale and number of biometric deployments around the world necessitates research into technologies which facilitate fast identification queries and high discriminative power. In this context, this article presents a biometric identification system which relies on a successive pre-filtering of the potential candidate list using multiple biometric modalities, coupled with a weighted score-level information fusion. The proposed system is evaluated in a series of experiments using a compound dataset constructed from several publicly available datasets; an open-set identification scenario is considered with the enrolment database containing 1,000 chimeric instances. This evaluation shows that the proposed system exhibits a significantly increased biometric performance w.r.t. a weighted score-level or rank-level fusion based baseline, while simultaneously providing a consequential computational workload reduction in terms of penetration rate. Lastly, it is worth noting that the proposed system could be flexibly employed in any multi-biometric identification system, irrespective of the chosen types of biometric characteristics and the encoding of their extracted features.

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Section: Biometric Information Fusionmentioning

confidence: 99%

Multi-Biometric Identification With Cascading Database Filtering

Drozdowski

Rathgeb

Mokros

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

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“…To provide the solution to address these risks, the survey of advanced algorithms using genetic programming, fuzzy set theory was designed by Jong Hyuk Park and Neil Yuwen Yen [17]. A comprehensive review of multi-biometric fusion methods for accessing IoT services was investigated [18]. However, the authentication accuracy was not concentrated.…”

Section: Related Workmentioning

confidence: 99%

Histogram of Neighborhood Tripartite Authentication with Fingerprint-based Biometrics for IoT Services

Kanchana¹

2019

IJCNC

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Internet of Things (IoT) and services is an interesting topic with a wide range of potential applications like smart home systems, health care, telemedicine, and intelligent transportation. Traditionally, key agreement schemes have been evaluated to access IoT services which are highly susceptible to security. Recently, Biometric-based authentication is also used to access IoT services and devices. They are involving a larger amount of memory with increased running time and found to be computationally infeasible. To provide robust authentication for IoT services, Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics (HNTA-FB) for IoT services is proposed in this paper. This proposed HNTA-FB method uses binary patterns and a histogram of features to extract the region of interest. To reduce the memory requirements while providing access to IoT services, Histogram of Neighborhood Binary Pattern Pre-processing (HNBPP) model is proposed. The discriminative power of Neighbourhood Binary Pattern Registration (NBPR) is integrated with the normalized sparse representation based on the histogram. Additionally, this work presents a new Tripartite User Authentication model for fingerprint biometric template matching process. When compared with different state-of-the-art methods, the proposed method depicts significantly improved performance in terms of matching accuracy, computational overhead and execution speed and is highly effective in delivering smart home services.

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“…On the contrary, behavioral characteristics deal with features observed from human action. Examples of human action are gait, voice, and signature [2]. By using biometric methods, the problems faced in traditional methods as mentioned above can be solved.…”

Section: Introductionmentioning

confidence: 99%

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Hoo

Ibrahim

2019

Journal of Sensors

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The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

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The Fall of One, the Rise of Many: A Survey on Multi-Biometric Fusion Methods

Cited by 68 publications

References 264 publications

Multi-Biometric Identification With Cascading Database Filtering

Multi-Biometric Identification With Cascading Database Filtering

Histogram of Neighborhood Tripartite Authentication with Fingerprint-based Biometrics for IoT Services

Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

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