Near-Infrared Illumination Add-On for Mobile Hand-Vein Acquisition

Debiasi, Luca; Kauba, Christof; Prommegger, Bernhard; Uhl, Andreas

doi:10.1109/btas.2018.8698575

Cited by 15 publications

(13 citation statements)

References 20 publications

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“…Only several works make use of smartphones but modifying their hardware: the infrared blocking filter is physically removed from the camera. These studies are based on the hand palm vein [15] and the hand dorsal vein [16] modalities.…”

Section: Related Workmentioning

confidence: 99%

Vein Biometric Recognition on a Smartphone

Garcia-Martin

Sánchez-Reillo

2020

IEEE Access

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Human recognition on smartphone devices for unlocking, online payment, and bank account verification is one of the significant uses of biometrics. The exponential development and integration of this technology have been established since the introduction in 2013 of the fingerprint mounted sensor in the Apple iPhone 5s by Apple Inc.© (Motorola© Atrix was previously launched in 2011). Nowadays, in the commercial world, the main biometric variants integrated into mobile devices are fingerprint, facial, iris, and voice. In 2019, LG© Electronics announced the first mobile exhibiting vascular biometric recognition, integrated using the palm vein modality: LG© G8 ThinQ (hand ID). In this work, in an attempt to become the first research-embedded approach to smartphone vein identification, a novel wrist vascular biometric recognition is designed, implemented, and tested on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 devices. The near-infrared camera mounted for facial recognition on these devices accounts for the hardware employed. Two software algorithms, TGS-CVBR® and PIS-CVBR®, are designed and applied to a database generation and the identification task, respectively. The database, named UC3M-Contactless Version 2 (UC3M-CV2), consists of 2400 contactless infrared images from both wrists of 50 different subjects (25 females and 25 males, 100 individual wrists in total), collected in two separate sessions with different environmental light conditions. The vein biometric recognition, using PIS-CVBR®, is based on the SIFT®, SURF®, and ORB algorithms. The results, discussed according to the ISO/IEC 19795-1:2019 standard, are promising and pave the way for contactless real-time-processing wrist recognition on smartphone devices. INDEX TERMS vein biometric recognition, smartphone, wrist vascular biometric recognition, contactless database, biometrics on mobile devices, near-infrared camera, Xiaomi© Pocophone F1, Xiaomi© Mi 8, SIFT® (Scale-Invariant Feature Transform), SURF® (Speeded Up Robust Features).

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Section: Related Workmentioning

confidence: 99%

Vein Biometric Recognition on a Smartphone

Garcia-Martin

Sánchez-Reillo

2020

IEEE Access

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“…The mobile phone in use is a modified Nexus 5 smart phone where the NIR blocking filter was removed. For sufficient NIR light, a custom build illumination add-on [23] was added to the phone. Both, hand and finger veins, have been acquired from the palmar and dorsal view.…”

Section: Vascular Biometricsmentioning

confidence: 99%

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT – a case study

et al. 2020

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PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT is an EU project funded by the Horizon 2020 research and Innovation Programme. The main aim of PROTECT was to build an advanced biometric-based person identification system that works robustly across a range of border crossing types and that has strong user-centric features. This work presents the case study of the multibiometric verification system developed within PROTECT. The system has been developed to be suitable for different borders such as air, sea, and land borders. The system covers two use cases: the walk-through scenario, in which the traveller is on foot; the drive-through scenario, in which the traveller is in a vehicle. Each deployment includes a different set of biometric traits and this paper illustrates how to evaluate such multibiometric system in accordance with international standards and, in particular, how to overcome practical problems that may be encountered when dealing with multibiometric evaluation, such as different score distributions and missing scores.

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“…The authors in [16] developed an add-on nearinfrared illumination for hand veins recognition using mobile. Google Nexus 5 smartphone is used, and its camera is modified to be sensitive to infrared by EigenImaging Inc.…”

Section: Related Workmentioning

confidence: 99%

“…It takes a long execution time which is not suitable for real-time applications. Also in [16], frequency domain filters (such as HFE) are used in the enhancement process. This resulted in a significant drop in the frame rate.…”

Section: System Evaluationmentioning

confidence: 99%

“…Table 4 reports the comparative results of the proposed prototype with other existing systems presented in [12,13,16,19,22] in terms of hardware component used, the number of sample data, characteristics of sample data, and the total cost. The systems presented in [16,19,22] used the hardware components for collecting datasets for recognition purposes. These systems depend on an external processing unit for running their algorithms which leads to an increase in cost.…”

Section: System Evaluationmentioning

confidence: 99%

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Digital Vein Mapping Using Augmented Reality

Taha¹,

Ibrahim²,

Zayed³

2020

IJIES

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Vein detection is an important issue for the medical field. There are some commercial devices for detecting veins using infrared radiation. However, most of these commercial solutions are cost-prohibitive. Recently, veins detection has attracted much attention from research teams. The main focus is on developing real-time systems with low-cost hardware. Systems developed to reduce costs suffer from low frame rates. This, in turn, makes these systems not suitable for real-world applications. On the other hand, systems that use powerful processors to produce high frame rates suffer from high costs and a lack of mobility. In this paper, a real-time vein mapping prototype using augmented reality is proposed. The proposed prototype provides a compromised solution to produce high frame rates with a low-cost system. It consists of a USB camera attached to an Android smartphone used for real-time detection. Infrared radiation is employed to differentiate the veins using 20 Infrared Light Emitting Diodes (LEDs). The captured frames are processed to enhance vein detection using light computational algorithms to improve real-time processing and increase frame rate. Finally, the enhanced view of veins appears on the smartphone screen. Portability and economic cost are taken into consideration while developing the proposed prototype. The proposed prototype is tested with people of different ages and gender, as well as using mobile devices of different specifications. The results show a high vein detection rate and a high frame rate compared to other existing systems.

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Near-Infrared Illumination Add-On for Mobile Hand-Vein Acquisition

Cited by 15 publications

References 20 publications

Vein Biometric Recognition on a Smartphone

Vein Biometric Recognition on a Smartphone

PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT – a case study

Digital Vein Mapping Using Augmented Reality

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