Vein Biometric Recognition on a Smartphone

Garcia-Martin, Raul; Sánchez-Reillo, Raúl

doi:10.1109/access.2020.3000044

Cited by 32 publications

(38 citation statements)

References 25 publications

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“…The results, discussed according to the ISO/IEC 19795-1 standard [8], are obtained on a unique contactless wrist database captured on smartphones, UC3M-CV2 [7]. To compare them with the current State-of-the-Art, other noncontact and contact datasets are employed, UC3M-CV1 [4] and PUT [9], respectively.…”

Section: B Contributionsmentioning

confidence: 99%

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Deep Learning for Vein Biometric Recognition on a Smartphone

Garcia-Martin

Sánchez-Reillo

2021

IEEE Access

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The ongoing COVID-19 pandemic has pointed out, even more, the important need for hygiene contactless biometric recognition systems. Vein-based devices are great non-contact options although they have not been entirely well-integrated in daily life. In this work, in an attempt to contribute to the research and development of these devices, a contactless wrist vein recognition system with a reallife application is revealed. A Transfer Learning (TL) method, based on different Deep Convolutional Neural Networks architectures, for Vascular Biometric Recognition (VBR), has been designed and tested, for the first time in a research approach, on a smartphone. TL is a Deep Learning (DL) technique that could be divided into networks as feature extractor, i.e., using a pre-trained (different large-scale dataset) Convolutional Neural Network (CNN) to obtain unique features that then, are classified with a traditional Machine Learning algorithm, and fine-tuning, i.e., training a CNN that has been initialized with weights of a pre-trained (different large-scale dataset) CNN. In this study, a feature extractor base method has been employed. Several architecture networks have been tested on different wrist vein datasets: UC3M-CV1, UC3M-CV2, and PUT. The DL model has been integrated on the Xiaomi© Pocophone F1 and the Xiaomi© Mi 8 smartphones obtaining high biometric performance, up to 98 % of accuracy and less than 0.4 % of EER with a 50-50 % train-test on UC3M-CV2, and fast identification/verification time, less than 300 milliseconds. The results infer, high DL performance and integration reachable in VBR without direct user-device contact, for real-life applications nowadays.

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Section: B Contributionsmentioning

confidence: 99%

“…Another example of non-contact wrist VBR, embedded into a smartphone, in this case, was recently stated in [7] for use in future online payments, bank account access, and screen unlocking.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Vein Biometric Recognition on a Smartphone

Garcia-Martin

Sánchez-Reillo

2021

IEEE Access

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“…Moreover, it uses a new scaleorientation-invariant algorithm, based on Oriented FAST and Rotated BRIEF (ORB), Speeded Up The same authors used the same approach in [21], but with a modification. They use smartphones for capturing and processing their algorithms TGS and PIS in [22]. The smartphones used were Xiaomi Pocophone f1© and Xiaomi Mi 8©.…”

Section: Related Workmentioning

confidence: 99%

“…This affects the frame rate (an average frame rate of 12.75 fps). Furthermore, the techniques used in [22] are CLAHE, median, Gaussian, average, feature selection, and feature matching. Only preprocessing was performed to be compared with other results.…”

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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Design and Development of the Optimal Technique for the Enhancement of Contrast and Brightness Quality of Hand Capture Images Using Wavelet Transformation

Mahalakshmi¹,

Sheela²

2021

Artificial Intelligence in Intelligent Systems

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Vein Biometric Recognition on a Smartphone

Cited by 32 publications

References 25 publications

Deep Learning for Vein Biometric Recognition on a Smartphone

Deep Learning for Vein Biometric Recognition on a Smartphone

Digital Vein Mapping Using Augmented Reality

Design and Development of the Optimal Technique for the Enhancement of Contrast and Brightness Quality of Hand Capture Images Using Wavelet Transformation

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