Finger-vein verification based on the curvature in Radon space

Qin, Huafeng; He, Xiping; Yao, Xingyan; Li, Hongbing

doi:10.1016/j.eswa.2017.03.068

Cited by 66 publications

(33 citation statements)

References 17 publications

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“…In the classic repeated line tracking approach [24], two global thresholds (i.e., 85 and 175) are used to divide a image into three regions for matching. Some curvature-based approaches [14,15,23] enhanced vein patterns by computing the curvature of all pixels and an empirical threshold is employed to encode resulting enhancement image. For the finger-vein verification, the primary target of feature encoding is to improve performance, mainly verification error rates.…”

Section: Supervised Feature Encodingmentioning

confidence: 99%

“…Therefore, many models are built to detect the valley for vein pattern extraction. For instance, the curvature is sensitive to valley, so various approaches are proposed to enhance the vein patterns by computing mean curvature [14], difference curvature [23], and maxim curvature [15] of pixels in an image. In [24][25][26][27], the vein patterns are detected by computing the depth of the valley.…”

Section: Introductionmentioning

confidence: 99%

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Finger-Vein Verification Based on LSTM Recurrent Neural Networks

Qin

Wang

2019

Applied Sciences

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Finger-vein biometrics has been extensively investigated for personal verification. A challenge is that the finger-vein acquisition is affected by many factors, which results in many ambiguous regions in the finger-vein image. Generally, the separability between vein and background is poor in such regions. Despite recent advances in finger-vein pattern segmentation, current solutions still lack the robustness to extract finger-vein features from raw images because they do not take into account the complex spatial dependencies of vein pattern. This paper proposes a deep learning model to extract vein features by combining the Convolutional Neural Networks (CNN) model and Long Short-Term Memory (LSTM) model. Firstly, we automatically assign the label based on a combination of known state of the art handcrafted finger-vein image segmentation techniques, and generate various sequences for each labeled pixel along different directions. Secondly, several Stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) models are independently trained on the resulting sequences. The outputs of various SCNN-LSTMs form a complementary and over-complete representation and are conjointly put into Probabilistic Support Vector Machine (P-SVM) to predict the probability of each pixel of being foreground (i.e., vein pixel) given several sequences centered on it. Thirdly, we propose a supervised encoding scheme to extract the binary vein texture. A threshold is automatically computed by taking into account the maximal separation between the inter-class distance and the intra-class distance. In our approach, the CNN learns robust features for vein texture pattern representation and LSTM stores the complex spatial dependencies of vein patterns. So, the pixels in any region of a test image can then be classified effectively. In addition, the supervised information is employed to encode the vein patterns, so the resulting encoding images contain more discriminating features. The experimental results on one public finger-vein database show that the proposed approach significantly improves the finger-vein verification accuracy.

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Section: Supervised Feature Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finger-Vein Verification Based on LSTM Recurrent Neural Networks

Qin

Wang

2019

Applied Sciences

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“…palm print, iris, fingerprint and face (ii) Features that are intrinsic i.e. palm vein, hand vein, and finger vein [4].…”

Section: Introductionmentioning

confidence: 99%

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

Jumaa¹,

Zidan²

2019

PEN

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This paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation.

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“…Several types of biometric techniques have been presented based on these anatomic/behavioral features such as fingerprint, palm print, hand veins, finger veins, palm veins, foot vein, iris, gait, DNA recognition, palates, voice recognition, facial expression, heartbeat, signature, body language, and face shape [2]. These biometric recognition approaches can be divided into two categories: (i) extrinsic biometric features (palm print, iris, fingerprint, face) and (ii) intrinsic biometric features (palm vein, hand vein, and finger vein) [3]. Extrinsic features are more visible and have more adverse factors as compared to the intrinsic features.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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Finger-vein verification based on the curvature in Radon space

Cited by 66 publications

References 17 publications

Finger-Vein Verification Based on LSTM Recurrent Neural Networks

Finger-Vein Verification Based on LSTM Recurrent Neural Networks

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

A Systematic Review of Finger Vein Recognition Techniques

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