A Novel Method for Finger Vein Recognition

Zeng, Junying; Yao, Chen; Qin, Chuanbo; Wang, Fan; Gan, Junying; Zhai, Yongjie; Zhu, Boyuan

doi:10.1007/978-3-030-31456-9_6

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Our proposed methods are compared with previous ones (from the state of the art). Indeed, our FV unimodal recognition system is compared with Zeng et al method (Squeezenet) [12], Fang et al method (selective network) [14], and Hong et al method (VGG16) [13]. Similarly, the FKP unimodal recognition system is compared with Zhai et al [16] methods ( AlexNet and Batch‐normalised CNN), Chlaoua et al [17] methods (PCANet and SVM), and L. Fei et al method (DDBFL algorithm) [18].…”

Section: Resultsmentioning

confidence: 99%

“…In addition, because of the contactless sensor, reference recorded FV information and imprecise subject finger position during the identification process could not match due to finger rotation and translation. [12][13][14]. Finger shape also named finger geometry biometric (third finger biometric) includes the finger width, length, and thickness.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Daas

Yahi

Bakir

et al. 2020

IET Image Processing

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Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high-security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to select separate features descriptors from each unimodal biometrics modality. After that, the authors combine them using the proposed fusion approaches were support vector machine or Softmax applies as classifiers to increase the proposed system security. The efficiency of the proposed algorithms is tested using publicly available biometrics databases. The experimental results show that the proposed fusion architectures achieve an accuracy of 99.89% and an equal error rate of 0.05%. The obtained results indicate that the biometrics recognition system using deep learning is secure, robust, and reliable.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Daas

Yahi

Bakir

et al. 2020

IET Image Processing

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“…The majority of these models used for finger vein recognition typically require a 3-channel input. Zeng et al [24] propose to combine a pair of finger vein images with their corresponding difference image in these channels, to reinforce the learning of intra-class variations. In contrast, Song et al [25] opted for a different strategy, utilising a composite image of finger image pairs instead of a different image.…”

Section: Background and Related Workmentioning

confidence: 99%

A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches

Arıcan,

Veldhuis,

Spreeuwers

et al. 2024

IET Biometrics

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Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons.

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“…For this study, thanks to the transfer learning approach, its architecture was changed. Today, it finds application areas in many scientific studies [37][38][39].…”

Section: Squeezenet Pre-trained Dcnn Modelmentioning

confidence: 99%

Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification

Noğay¹

2021

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Fingerprint pattern recognition is of great importance in forensic examinations and in helping diagnose some diseases. The automatic realization of fingerprint recognition processes can take time due to the feature extraction process in classical machine learning or deep learning methods. In this study, the effective use of deep convolutional neural networks (DCNN) in fingerprint pattern recognition and classification, in which feature extraction takes place automatically, was examined experimentally and comparatively. Five DCNN models have been designed and implemented with a transfer learning approach. Four of these five models are Alexnet, Googlenet, Resnet-18, and Squeezenet pre-trained DCNN models. The fifth model is the DCNN model designed from the ground up. It was concluded that the designed DCNN models can be used effectively in fingerprint recognition and classification, and that fast results can be obtained and generalized with advanced DCNN models.

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A Novel Method for Finger Vein Recognition

Cited by 4 publications

References 14 publications

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion

A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches

Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification

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