Finger Vein Recognition With Anatomy Structure Analysis

Yang, Gongping; Yin, Yilong; Xi, Xiaoming

doi:10.1109/tcsvt.2017.2684833

Cited by 126 publications

(64 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some classical approaches such as RLT [4], LMC [7], WLD [8] and Gabor filter method [37] are carried out on two public datasets. Besides, the state-of-the-art conventional method, namely ASAVE [38], is also conducted in our experiments. Based on the labeling scheme in (4), we extract the finger vein patterns and obtain the binarized map L(x, y) computed by combining the segmentation outputs of the three baseline methods (RLT, LMC and WLD).…”

Section: B Experimental Settingmentioning

confidence: 99%

FV-GAN: Finger Vein Representation Using Generative Adversarial Networks

Yang

Hui

Chen

et al. 2019

IEEE Trans.Inform.Forensic Secur.

100

View full text Add to dashboard Cite

In finger vein verification, the most important and challenging part is to robustly extract finger vein patterns from low-contrast infrared finger images with limited a priori knowledge. Although recent convolutional neural network (CNN)-based methods for finger vein verification have shown powerful capacity for feature representation and promising perspective in this area, they still have two critical issues to address. First, these CNNbased methods unexceptionally utilize fully connected layers, which restricts the size of finger vein images to process and increases the processing time. Second, the capacity of CNN for feature representation generally suffers from the low quality of finger vein ground-truth pattern maps for training, particularly due to outliers and vessel breaks. To address these issues, in this paper we propose a novel approach termed FV-GAN to finger vein extraction and verification, based on generative adversarial network (GAN), as the first attempt in this area. Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and pattern maps rather than the direct mapping between them, with the aim at achieving stronger robustness against outliers and vessel breaks. Moreover, FV-GAN adopts fully convolutional networks as the basic architecture, and discards fully connected layers, which relaxes the constraint on the input image size and reduces the computational expenditure for feature extraction. Furthermore, we design an adversarial training strategy and propose a hybrid loss function for FV-GAN. The experimental results on two public databases show a significant improvement by FV-GAN in finger vein verification in terms of both verification accuracy and equal error rate.Index Terms-Finger vein verification, pattern extraction, convolutional neural network (CNN), generative adversarial network (GAN), cycle-consistent adversarial network (CycleGAN), deep convolutional generative adversarial network (DCGAN), U-Net.

show abstract

Section: B Experimental Settingmentioning

confidence: 99%

FV-GAN: Finger Vein Representation Using Generative Adversarial Networks

Yang

Hui

Chen

et al. 2019

IEEE Trans.Inform.Forensic Secur.

100

View full text Add to dashboard Cite

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%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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

“…In the region growth approach [27], both depth and symmetry of valley are combined to extract vein pattern. Recently, according to the anatomical knowledge, some characteristics of finger-vein structure, e.g., directionality, continuity, width variability, smoothness, and solidness are taken into account for finger-vein texture extraction in [28].…”

Section: Introductionmentioning

confidence: 99%

Finger-Vein Verification Based on LSTM Recurrent Neural Networks

Qin

Wang

2019

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Finger Vein Recognition With Anatomy Structure Analysis

Cited by 126 publications

References 37 publications

FV-GAN: Finger Vein Representation Using Generative Adversarial Networks

FV-GAN: Finger Vein Representation Using Generative Adversarial Networks

State of the Art in Vascular Biometrics

Finger-Vein Verification Based on LSTM Recurrent Neural Networks

Contact Info

Product

Resources

About