Dorsal hand vein recognition based on directional filter bank

Lee, Jen-Chun; Lo, Tsung-Ming; Chang, Chien-Ping

doi:10.1007/s11760-014-0714-8

Cited by 35 publications

(17 citation statements)

References 16 publications

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“…We first discuss techniques relying on the extracted vascular structure. Lee et al [143] use a directional filter bank involving different orientations to extract vein patterns, and the minimum directional code is employed to encode line-based vein features into a binary code. Explicit background treatment is applied similar to the techniques used in [277] for palm veins.…”

Section: (Dorsal) Hand Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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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.

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Section: (Dorsal) Hand Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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“…Lee et al [6] suggested a directional filter include different alignments that cutting hand vein patterns and encode hand vein features into binary code by the minimum directional filtering response (MDFR) and classification by Hamming Distance (HD). Also, there are many areas that not contain the vein in the image, which are not important for hand vein identification.…”

Section: Related Workmentioning

confidence: 99%

“…As a summary, the systems presented in [5][6][7][8][9][10] extracting features by using machine learning techniques that need to be identified by an expert and then hand-coded as per the domain and data type. As well as in [11] images have been processed before the features extraction by extracting the region of interest (ROI) then applied histogram equalization and Gaussian smoothing filter.…”

Section: Related Workmentioning

confidence: 99%

“…In [5], authors used LBP+BC, and Graph feature extraction algorithms and FGM classifier they get accuracy rate of 99.27%. On the other hand, in [6] authors used MDFR feature extraction algorithms and HD classifier that get accuracy rate of 98.83%. In [7], the authors used CDSDP feature extraction algorithms with ANN and FMNN classifier and got an accuracy rate of 98.8%.…”

Section: Comparison With the State Of The Artmentioning

confidence: 99%

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Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches

Al-johania¹,

Elrefaei²

2019

IJIES

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In this paper, we propose a dorsal hand vein recognition system using Convolutional Neural Network (CNN). This system is automatically learned how to extract features from original image without preprocessing. The proposed system has two approaches: the first one is using the pre-trained CNN models (AlexNet, VGG16 and VGG19) for extracting features from 'fc6','fc7' and 'fc8' layers then using Error-Correcting Output Codes (ECOC) with Support Vector machine (SVM) and K-Nearest Neighbor (K-NN) algorithms for the classification. The second approach is using transfer learning with CNN (AlexNet, VGG16 and VGG19) models for both features' extraction and classification. The experiments applied on two different datasets: Dr. Badawi hand veins dataset that contains 500 image and BOSPPHORUS dorsal vein dataset that contains 1575 images. In the first approach experiment, the recognition accuracy of all models gives best result when features are extracted from 'fc6'. Also, the accuracy rate of the models that use ECOC with SVM for classification is higher than the models that use ECOC with KNN and the VGG19 model achieves better results in models that use ECOC with SVM. In the second approach experiment, the recognition accuracy for all models give best result when epoch number is 50 where Dr. Badawi dataset in VGG16 and AlexNet reaches to 100% recognition rate and BOSPPHORUS dataset reaches to 99.25 % recognition rate in VGG19. Finally, the discussion concluded that using transfer learning is giving more accuracy rate than using the pre-trained CNN models for extracting features.

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“…Wang et al [11] extracted LBP (local binary patterns) from a dorsal hand vein to build an automatic access control system. Based on the Gabor filter, Lee et al [37] designed directional filter banks to draw local patterns for dorsal hand vein recognition.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

Nie¹,

Zhang²,

Zhao³

2019

Applied Sciences

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Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.

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Dorsal hand vein recognition based on directional filter bank

Cited by 35 publications

References 16 publications

State of the Art in Vascular Biometrics

State of the Art in Vascular Biometrics

Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

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