Dorsal hand vein recognition based on convolutional neural networks

Wan, Haipeng; Chen, Lei; Song, Hong; Yang, Jian

doi:10.1109/bibm.2017.8217830

Cited by 42 publications

(29 citation statements)

References 15 publications

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“…A system that uses deep learning for dorsal hand vein recognition was proposed by Wan et al [11] that depends on CNN, and extracts image by the region of interest (ROI) then preprocess this image with histogram equalization and Gaussian smoothing filter. The system extracts feature by using Convolution Architecture for Feature Extraction (Reference-CaffeNet), AlexNet and VGG and using logistic regression for classification.…”

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 [10] the authors used LBP method and graph matching for classification and got accuracy rate of 96.67%. In [11] the authors use deep learning for feature extraction and logistic regression classifier and got accuracy rate of 99.33%. 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: Comparison With the State Of The Artmentioning

confidence: 99%

“…In [11] the authors use deep learning for feature extraction and logistic regression classifier and got accuracy rate of 99.33%. 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. However, the proposed system uses original images without preprocessing then recognition is based on CNN depth models that select and express the depth feature of the image automatically.…”

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With the State Of The Artmentioning

confidence: 99%

Section: Comparison With the State Of The Artmentioning

confidence: 99%

See 2 more Smart Citations

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

Al-johania¹,

Elrefaei²

2019

IJIES

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“…Há mais de um século, diferentes modalidades de biometria têm sido estudadas e aplicadas para propósitos de segurança e acesso automático, sendo as mais utilizadas comercialmente baseadas nas seguintes informações biométricas: impressão digital [4], [5]; face [6], [7]; íris [7]; voz [8], [9]; assinatura [10]; palma da mão [11]. Além de diferentes modalidades propostas nos últimos anos, baseadas em outras informações, por exemplo: padrão de digitação [12]; padrão de caminhada [13], [14]; orelha [15]; veias da mão [16]; vasos da retina [17]. Um sistema biométrico pode ser genericamente descrito conforme figura 1.…”

Section: Introductionunclassified

Biometria multimodal baseada nos sinais de voz e facial

Parada¹

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Multispectral hand features for secure biometric authentication systems

Bartuzi

Trokielewicz

2021

Concurrency and Computation

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Summary With rapid growth of mobile‐based computing, reliable security and user authentication methods are necessary. This article proposes a proof‐of‐concept biometric authentication method utilizing hand images collected in different light spectra. An analysis of similarity between pattern of blood vessels extracted from near‐infrared images and thermal images and assessment of the correlation between individual biometric features contained in each image type is also performed. Results indicate a large potential of using thermal images in biometrics more extensively than now. Also proposed and evaluated are biometric recognition methods based on images of the hand acquired in visible light, near infrared, and using thermal infrared sensors. Two approaches were used to assess information content in images of each type: one based on texture descriptor and one employing convolutional neural networks. In an evaluation gathering data from 104 subjects, the former yielded the lowest equal error rate (EER) of 7.44%, whereas the latter approach gave EER=0.03% for thermal images. Finally, fusion of different‐spectra modalities increases accuracy and further reduces EER to 0.01%. This is, to the authors' best knowledge, the first study exploring the concept of fusing different spectral representations of the human hand for the purpose of biometric recognition.

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Dorsal hand vein recognition based on convolutional neural networks

Cited by 42 publications

References 15 publications

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

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

Biometria multimodal baseada nos sinais de voz e facial

Multispectral hand features for secure biometric authentication systems

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