Multimodal biometrics for enhanced mobile device security

Gofman, Mikhail; Mitra, Sinjini; Cheng, Tsu-Hsiang Kevin; Smith, Nicholas

doi:10.1145/2818990

Cited by 27 publications

(21 citation statements)

References 12 publications

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“…An EER of 0.1% has been obtained for use in a quiet office, and an EER of 0.3-0.6% for use in a noisy cafeteria. The authors of [27] have shown the potential of multimodal biometrics for protecting consumer mobile devices from unauthorized access. They have implemented Proteus based on face and voice biometrics.…”

Section: Related Workmentioning

confidence: 99%

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

Cherifi

Amroun

Omar

2021

Multimed Tools Appl

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Nowadays, authentication is required for both physical access to buildings and internal access to computers and systems. Biometrics are one of the emerging technologies used to protect these highly sensitive structures. However, biometric systems based on a single trait enclose several problems such as noise sensitivity and vulnerability to spoof attacks. In this regard, we present in this paper a fully unobtrusive and robust multimodal authentication system that automatically authenticates a user by the way he/she answers his/her phone, after extracting ear and arm gesture biometric modalities from this single action. To deal the challenges facing ear and arm gesture authentication systems in real-world applications, we propose a new method based on image fragmentation that makes the ear recognition more robust in relation to occlusion. The ear feature extraction process has been made locally using Local Phase Quantization (LPQ) in order to get robustness with respect to pose and illumination variation. We also propose a set of four statistical metrics to extract features from arm gesture signals. The two modalities are combined on score-level using a weighted sum. In order to evaluate our contribution, we conducted a set of experiments to demonstrate the contribution of each of the two biometrics and the advantage of their fusion on the overall performance of the system. The multimodal biometric system achieves an equal error rate (EER) of 5.15%.

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Section: Related Workmentioning

confidence: 99%

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

Cherifi

Amroun

Omar

2021

Multimed Tools Appl

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“…Because almost all mobile devices integrate biometric sensors such as cameras, microphones or fingerprint scanners, the next natural step is to fuse their information using multimodal biometrics, which can provide higher accuracy and increase the difficulty of spoofing attacks [19].…”

Section: Personal Devicesmentioning

confidence: 99%

Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Genovese

Muñoz

Piuri

et al. 2018

Lecture Notes in Computer Science

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Biometric systems are the ensemble of devices, procedures, and algorithms for the automatic recognition of individuals by means of their physiological or behavioral characteristics. Although biometric systems are traditionally used in high-security applications, recent advancements are enabling the application of these systems in less-constrained conditions with non-ideal samples and with real-time performance. Consequently, biometric technologies are being increasingly used in a wide variety of emerging application scenarios, including public infrastructures, e-government, humanitarian services, and user-centric applications. This chapter introduces recent biometric technologies, reviews emerging scenarios for biometric recognition, and discusses research trends.

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“…Together with the advances in embeddable high-quality sensors, those trends have sparked interest in (single and multi modal) mobile biometrics, which has become an active area of research and product development (see e.g. [16], [17], [18], [19], [20]).…”

Section: Introductionmentioning

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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Multimodal biometrics for enhanced mobile device security

Abstract: Fusing information from multiple biometric traits enhances authentication in mobile devices.

Cited by 27 publications

References 12 publications

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

Robust multimodal biometric authentication on IoT device through ear shape and arm gesture

Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Multi-Biometric Identification With Cascading Database Filtering

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