PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Thapar, Daksh; Jaswal, Gaurav; Nigam, Aditya; Kanhangad, Vivek

doi:10.1109/isba.2019.8778623

Cited by 49 publications

(27 citation statements)

References 15 publications

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“…Reference Method EER Kang W., Wu Q [10] Improved LBP 0.00267 Zhou Y., Kumar A. [28] NMRT 0.0051 Hessian phase 0.0144 Thapar D., Jaswal G., Nigam A., Kanhangad V. [24] PVSNet 0.0371 Bhilare S., Jaswal G., Kanhangad V., Nigam A. [1] Deep matching 0.0261 Raghavendra R., Busch C. [19] LD-KDA-SRC 0.1010±0.0102…”

Section: Resultsmentioning

confidence: 99%

Palm Vein Recognition Algorithm using Multilobe Differential Filters

Safronova¹,

Павельева²,

Pavelyeva³

2019

GraphiCon'2019 Proceedings. Volume 1

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In this article the new algorithm for palm vein recognition using multilobe differential filters is proposed. After palm vein image preprocessing vein structure is detected based on principal curvatures. The image is considered as a surface in a three-dimensional space. Some vein points are selected using the maximum principal curvature values, and the other vein points are found from starting points by moving along the direction of minimum principal curvature. Multilobe differential filters are used to extract feature maps for vein images. These filters are flexible in terms of basic lobe choice and spatial configuration of lobes. The multilobe differential filters used in the article simulate vein branch points, and Gaussian kernel is used as the basic lobe. The normalized root-mean-square error is applied for image matching. Experimental results using CASIA multi-spectral palmprint image database demonstrate the effectiveness of the proposed method. The value of EER=0.01693 is obtained.

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Section: Resultsmentioning

confidence: 99%

Palm Vein Recognition Algorithm using Multilobe Differential Filters

Safronova¹,

Павельева²,

Pavelyeva³

2019

GraphiCon'2019 Proceedings. Volume 1

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show abstract

“…CNN feature learning and transfer approach on hand dorsal vein was performed in [33]. Palm vein authentication using CNN with PVSNet architecture for training to deal with the need for huge amount of training data in deep learning [34]. Researchers have also combined hand vein traits to form multimodal biometric systems such as in [35] where a multimodal system with CNN based deep-learning using finger-vein and finger shape as traits was proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

2020

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Multimodal biometric systems are preferred as a defense compared to unimodal systems. This study introduces an open access multimodal vein database named FYO with each letter dedicated to each author's name. The database involves three biometric traits; palm vein, dorsal vein and wrist vein of the same individuals, to explore and enhance research in the area of using these traits to create a spoof-proof multimodal authentication system. The vein images of FYO are acquired using medical vein finder in a controlled environment. Comparisons are performed to show the differences with the existing well known databases and the state-of-the-art recognition algorithms. Hand-crafted feature extractors such as Binarized Statistical Image Features (BSIF), Gabor filter and Histogram of Oriented Gradients (HOG) are applied to show the viability of the vein datasets. Additionally, a deep learning based Convolutional Neural Networks (CNN) architecture is proposed with two models using decision-level fusion of palmar, dorsal and wrist biometric traits on vein images. Unimodal systems, multimodal systems and the proposed architecture are tested on several vein datasets including palmar, dorsal and wrist vein images. Experimental results based on accuracy and computation time on our FYO datasets showed competitive output with that of other databases such as Tongji Contactless Palm Vein database, VERA, PUT, Badawi and Bosphorus hand vein databases. Moreover, the proposed CNN architecture on three vein biometric traits show superior performance compared to hand-crafted methods. INDEX TERMS Data fusion, deep learning, hand-crafted features, vein recognition, multimodal biometrics.

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“…Zhang et al [17] released a new touchless palm vein database, and used the method of PalmRCNN for palm vein recognition. Lefkovits et al [92] applied four CNNs for palm vein identification including AlexNet, VGG-16, ResNet-50, and SqueezeNet. Thapar et al [93] proposed the method of PVSNet.…”

Section: Inception_resnet_v2mentioning

confidence: 99%

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Jia

Gao

Xia

et al. 2020

Int. J. Autom. Comput.

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Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

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PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Cited by 49 publications

References 15 publications

Palm Vein Recognition Algorithm using Multilobe Differential Filters

Palm Vein Recognition Algorithm using Multilobe Differential Filters

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

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