Convolutional Neural Network for Finger-Vein-Based Biometric Identification

Das, Rig; Piciucco, Emanuela; Maiorana, Emanuele; Campisi, Patrizio

doi:10.1109/tifs.2018.2850320

Cited by 253 publications

(155 citation statements)

References 51 publications

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“…Radzi et al used a model of reduced-complexity (a four-layered CNN) classifier, with fused convolutional-subsampling architecture for finger vein recognition [35]. Itqan et al performed finger vein recognition using a CNN classifier of similar structure [29], and Das et al [5] correspondingly proposed a CNN classifier for finger vein identification. This approach, however, has significant drawbacks in case new users have to be enrolled as the networks have to be retrained, which is not practical.…”

Section: Cnn-based Finger Vein Recognitionmentioning

confidence: 99%

Improved CNN-Segmentation-Based Finger Vein Recognition Using Automatically Generated and Fused Training Labels

Jalilian

Uhl

2019

Advances in Computer Vision and Pattern Recognition

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We utilise segmentation-oriented CNNs to extract vein patterns from nearinfrared finger imagery and use them as the actual vein features in biometric finger vein recognition. As the process to manually generate ground-truth labels required to train the networks is extremely time-consuming and error prone, we propose several models to automatically generate training data, eliminating the needs for manually annotated labels. Furthermore, we investigate label fusion between such labels and manually generated labels. Based on our experiments, the proposed methods are also able to improve the recognition performance of CNN-network-based feature extraction up to different extents.

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Section: Cnn-based Finger Vein Recognitionmentioning

confidence: 99%

Improved CNN-Segmentation-Based Finger Vein Recognition Using Automatically Generated and Fused Training Labels

Jalilian

Uhl

2019

Advances in Computer Vision and Pattern Recognition

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“…Image acquisition Several works propose multiple approaches for the extraction of traits and patterns from the finger veins [26][27][28][40][41][42][43][44][45][46][47]. Methods based on local patterns extract characteristics at the pixel level, including descriptors such as local binary patterns (LBP) [40,42] and their different variants [41,43,48,49].…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Other proposals use minutiae as identification features [40,45,52,53], however, their extraction is limited in finger vein images, which reduces the accuracy of the results. On the other hand, there are methods based on machine learning, mainly in the analysis of principal components (PCA) [44,46,54,55], linear discriminant analysis (LDA) [56], and, recently, some authors [28,[57][58][59] have proposed finger vein recognition methods based on deep learning approaches, which have been successfully applied and enhance finger vein recognition methods. In these cases, training images are not enough available for the configuration of the transformation matrix, so its operation is not satisfactory in this regard.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…The performance and quality of the results in the recognition of finger vein patterns have been improved by several approaches in recent years [17,27,28]. During the image acquisition process, the captured images present deformation effects due to the contactless capturing procedure, as is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…LBP-based approaches as local binary pattern (LBP) [42] [58], two-stream CNN (two-CNN) [59], and the very deep CNN (deeper-CNN) proposed in the work by the authors of [28]; and • deformation-based methods as the detection and correction method (DFVR) proposed by the authors of [61] and the baseline method [27]. From the results in Table 7, it can be seen that the recognition performance of deformation-based methods is significantly more accurate than the rest on both databases.…”

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confidence: 99%

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Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

et al. 2019

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Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

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A study on finger vein recognition with few samples based on residual connected meta‐learning

Zhang

Yan

et al. 2022

Computer Animation & Virtual

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A meta‐learning based method for finger vein recognition with few samples is proposed to overcome the problem of low recognition accuracy caused by the small number and variety of finger vein samples as well as fuzzy vein lines. The method is based on meta‐learning, incorporating multiscale features, and using the idea of residual networks to join meta‐learning to improve the recognition accuracy of finger vein images with few samples; to further improve its recognition ability, a differential map is constructed in the form of a differential between the finger vein image of singular value decomposition and finger vein image. We are the first to apply meta‐learning to the field of finger vein recognition, to our knowledge, and the experiments show that this approach is superior, with recognition accuracy of up to 99.13% for finger vein datasets with few‐shot samples.

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Convolutional Neural Network for Finger-Vein-Based Biometric Identification

Abstract: This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Created by The Institute of Electrical and Electronics Engineers (IEEE) for the benefit of humanity.

Cited by 253 publications

References 51 publications

Improved CNN-Segmentation-Based Finger Vein Recognition Using Automatically Generated and Fused Training Labels

Improved CNN-Segmentation-Based Finger Vein Recognition Using Automatically Generated and Fused Training Labels

Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

A study on finger vein recognition with few samples based on residual connected meta‐learning

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