On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks

In this work, we investigate the possibility of generating grayscale images of finger and hand vein patterns from their corresponding binary templates. This exercise would allow us to determine the invertibility of vascular templates, which has implications in biometric security and privacy. The transformation from binary features to a gray-scale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger-vein datasets and 3 hand-vein datasets. Results indicate that (a) it is possible to reconstruct the considered vascular images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization). The results of this research further underscore the need for properly securing biometric templates, even if they are of binary nature.

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“…Thus, we will extend our research to other feature types, including key-point based ones (e.g. DTFPM [44]) as well as CNN based ones [14], [37].…”

Section: Summary and Future Workmentioning

confidence: 99%

Inverse Biometrics: Generating Vascular Images From Binary Templates

Kauba

Kirchgasser

Mirjalili

et al. 2021

IEEE Trans. Biom. Behav. Identity Sci.

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“…The wrist VBR State-of-the-Art has been analyzed from the point of view of the recognition algorithms and techniques because the proposed acquisition hardware or system, a smartphone, is different and innovative. Furthermore, the absence of contact between the user and the capture system is a current stream of research that is starting to emerge, as is the case of [1] or [17] (finger vein modality).…”

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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“…The existing researches on vein recognition can be roughly divided into the following two categories: 1) Vein recognition based on gray features. It is very common to extract effective gray features by analyzing the highfrequency information of the vein images, where the highfrequency information can be acquired based on multi-scale analysis theories which include traditional wavelet transform [2][3], Bandelet transform [4], Gabor transform [5][6][7][8][9][10], Curvelet transform [11], Contourlet transformation [12][13], Histogram of Oriented Gridients (HOG) operator [14], spatial curve filtering [15], ridgelet transformation [16], Scale-Invariant Feature Transform (SIFT) [17] and some improved Gabor transform methods [18][19][20][21]; In recent years, some deep learning methods have also been gradually used in vein recognition such as deep neural networks [22] and convolution neural networks [23][24]; In addition, the gray statistical distribution based methods were also verified to be effective such as intensity distribution [25], hierarchical hyper-sphere model [26], sparse representation [27], gradient distribution [28], etc. 2) Vein recognition based on points and curves.…”

Section: Related Workmentioning

confidence: 99%

Vein Recognition Algorithm Based on Transfer Nonnegative Matrix Factorization

2020

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Most of the existing vein recognition algorithms are only effective for specified datasets, and once replacing the vein image acquisition device, i.e., the properties of the collected vein images are changed, the performance of the algorithm will be degraded greatly. Therefore, a transfer Nonnegative Matrix Factorization (NMF) based vein recognition algorithm is proposed, which makes vein features more universal. Its contributions are mainly reflected in the following two aspects: 1) The orthogonal constraint is imposed on the model to reduce the redundancy between feature bases and increase the difference between the features of different veins; 2) The differences between the vein features in different datasets are reduced based on Maximum Mean Difference (MMD) constraint, i.e., the knowledge of the source dataset is transferred to the target dataset well, and the universality of vein features can be improved. Experimental results show that the proposed algorithm outperforms state of the art methods on two dorsal hand vein datasets and two finger vein datasets.

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On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks

Cited by 85 publications

References 61 publications

Inverse Biometrics: Generating Vascular Images From Binary Templates

Inverse Biometrics: Generating Vascular Images From Binary Templates

Vein Biometric Recognition on a Smartphone

Vein Recognition Algorithm Based on Transfer Nonnegative Matrix Factorization

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