Recent Advances in Biometric Technology for Mobile Devices

Das, Abhijit; Galdi, Chiara; Han, Hu; Ramachandra, Raghavendra; Dugelay, Jean‐Luc; Dantcheva, Antitza

doi:10.1109/btas.2018.8698587

Cited by 29 publications

(14 citation statements)

References 55 publications

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“…Most frequently, this performance is achieved using power-hungry processors and graphics processing units (GPUs) [ 42 , 43 ]. While this cost in power and space may not be important in big data applications that require high precision, it is normally not acceptable in mobile or portable biometric systems [ 11 ], which require compact, power-efficient electronics.…”

Section: Related Workmentioning

confidence: 99%

“…One of the most popular biometric techniques is face recognition [ 2 ], which has abundant applications [ 3 , 4 , 5 ] in various areas, such as: (1) security, including identity verification [ 6 , 7 ], computer or mobile device unlock [ 7 , 8 ], criminal records search, and voter registration; (2) surveillance, such as cameras used on closed circuit television (CCTV) [ 9 ]; and (3) access control that could grant access to a specific place or an electronic account to a group of people [ 10 ] using their faces as a credential. As described by Das et al [ 11 ], there is a growing attention from the scientific community on mobile devices with biometric recognition. This attention is mainly fueled by the commercial interest in robust authentication methods for smartphones, laptops, tablets, and other mobile devices [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binary patterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen’s algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 μm mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 μm process and 76% on a 0.18 μm process with 32 μm × 32 μm pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a 150×80-pixel image in 94 μs, and consumes 71 mW of power.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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“…According to one report [37], the global biometric technology market was valued at 17.16 billion USD in 2018 and is expected to reach approximately 59.43 USD billion by 2025, growing at a compound annual growth rate of 19.42% between 2019 and 2025. The deployment of mobile biometrics is growing rapidly [38] and this is now considered a robust authentication method for virtually all mobile devices and smartphones [39], [40]. Table 1 compares some biometric technologies in terms of different aspects such as accuracy, performance, cost, measurability, and security level.…”

Section: Figure 1 Biometric Technologies Used In Applications In 2018mentioning

confidence: 99%

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

Shdefat

Mostafa

Saker

et al. 2021

IJEEI

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The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

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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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Recent Advances in Biometric Technology for Mobile Devices

Cited by 29 publications

References 55 publications

Face Recognition on a Smart Image Sensor Using Local Gradients

Face Recognition on a Smart Image Sensor Using Local Gradients

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

Multi-Biometric Identification With Cascading Database Filtering

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