Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: The impact of training data

Jalilian, Ehsaneddin; Uhl, Andreas

doi:10.1109/wifs.2018.8630794

Cited by 30 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Radzi et al [32] exploited a four-layer CNN to extract features and compared the Euclidean distance of features. And the latest methods of deep neural networks include light convolutional neural networks [33], two-stream convolutional neural networks [34] and fully convolution neural networks [35]. Qin and El-Yacoubi [36] proposed a deep representation based feature extraction method.…”

Section: Related Work a Finger Vein Recognition Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-ofthe-art methods in finger-vein classification.

show abstract

Section: Related Work a Finger Vein Recognition Methodsmentioning

confidence: 99%

“…The initialization settings of the Gabor filter are λ ∈ [30,35], θ = 0, ψ = 0, γ = 1, and 16 Gabor convolutions, and the convolution size is 61 × 61. In this experiment, the learning rate of parameter λ of the Gabor function is set to 0.01, and the learning rate of other parameters is set to 0.…”

Section: Learning Parameters λ and σ Of Gabor Filtermentioning

confidence: 99%

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As an extension of this work, [40] was engaged with an novel method to extract the depth feature of vein based on both short-term and long-term memory recurrent neural network, while Yang et al [41] introduced a finger vein segmentation model in light of the generative adversarial networks, which stands astonishing robustness to outliers and vessel breaks. Referring to [42]- [44], semantic segmentation convolutional neural network was optimized to directly abstract the actual finger-vein patterns from NIR finger images while Jalilian and Uhl [43] investigated the effects of fusion and combination training with different labels on finger vein recognition.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field

et al. 2020

View full text Add to dashboard Cite

Owing to the complexity of finger vein patterns in shape and spatial dependence, the existing methods suffer from an inability to obtain accurate and stable finger vein features. This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). Firstly, to reduce missing pixels during ROI extraction, the method of sliding window summation is employed to filter and adjusted with self-built tools. In addition, the traditional baselines are endowed with different weights to automatically assign labels. Secondly, the deformable convolution network, through replacing the plain counterparts in the standard U-Net mode, can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins' scales and shapes. Moreover, the above features can be further mined and accumulated by combining the recurrent neural network (RNN) and the residual network (ResNet). With the steps mentioned above, the fully convolutional neural network is constructed. Finally, the CRF with Gaussian pairwise potential conducts mean-field approximate inference as the RNN, and then is embedded as a part of the FCN, so that the model can fully integrate CRF with FCNs, which provides the possibility to involve the usual back-propagation algorithm in training the whole deep network end-to-end. The proposed models in this paper were tested on three public finger vein datasets SDUMLA, MMCBNU and HKPU with experimental results to certify their superior performance on finger-vein verification tasks compared with other equivalent models including U-Net.

show abstract

“…Techniques for RoI determination are typically described in the context of descriptions of the entire recognition toolchain. There are hardly papers dedicated to this [76,94,163,203,209,299] Binary vascular structure using semantic segmentation CNNs [91,[100][101][102] Minutiae [84,148,293] issue separately. A typical example is [287], where an inter-phalangeal joint prior is used for finger vein RoI localisation and haze removal methods with the subsequent application of Gabor filters are used for improving visibility of the vascular structure.…”

Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

“…Finally, semantic segmentation convolutional neural networks have been used to extract binary vascular structures subsequently used in classical binary template comparison. The first documented approach uses a combination of vein pixel classifier and a shallow segmentation network [91], while subsequent approaches rely on fully fledged deep segmentation networks and deal with the issue of training data generation regarding the impact of training data quality [100] and a joint training [30,40,60,98,144,228] Deep learning (entire toolchain) learning sample similarity [89,284] with manually labelled and automatically generated training data [101]. This book contains a chapter extending the latter two approaches [102].…”

Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

Self Cite

View full text Add to dashboard Cite

The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

show abstract

Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: The impact of training data

Cited by 30 publications

References 20 publications

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field

State of the Art in Vascular Biometrics

Contact Info

Product

Resources

About