A Database with ROI Extraction for Studying Fusion of Finger Vein and Finger Dorsal Texture

Yang, Wenming; Qin, Chuan; Liao, Qingmin

doi:10.1007/978-3-319-12484-1_30

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…We use nine public datasets for evaluation experiments: SDUMLA [ 29 ], FV_USM [ 30 ], HKPU_FID [ 31 ], SCUT_RIFV [ 14 ], PLUSVein [ 32 ], MMCBNU_6000 [ 33 ], UTFVP [ 34 ], VERA [ 13 ], and THU_FVFVD [ 35 ].…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Li,

Gong,

Zheng

2024

Sensors

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FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Li,

Gong,

Zheng

2024

Sensors

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“…All of the experiments were conducted based on publicly available databases. The databases that we used were FV_USM [38], SDUMLA_HMT [39], and THU_FVT2 [40]. The main reason we used the specific database was that most of the established method in the finger vein recognition had been evaluated by these databases and fair comparison can be done between the proposed and establish methods.…”

Section: Resultsmentioning

confidence: 99%

“…The third database was the THU_FVT2, prepared by Tsinghua University [40]. This database contained 1220 images acquired from 610 classes of training and testing sessions.…”

Section: A Databasesmentioning

confidence: 99%

A New Filter Generation Method in PCANet for Finger Vein Recognition

Kamaruddin

Rosdi

2019

IEEE Access

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Currently, the used of deep learning method has attracted widespread attention in machine learning, especially in Biometric. Many deep learning methods have been proposed like convolutional neural network (CNN), AlexNet and principal component analysis network (PCANet). Among the methods employed, PCANet is believed to be the most effective method because of its promising performance in biometric. However, the current filter generation approach in PCANet does not consider the characteristics of the image, such as vein lines for finger vein recognition. To address this limitation, we proposed a new filter generation method that can consider the essential features of the image, such as the use of vein lines for finger vein recognition. The filter of the proposed method was generated by finding the correlation between two view images, which was original grayscale image and vein line image using canonical correlation analysis (CCA) method. Then, we evaluated this proposed method with the three public finger vein image database, FV-USM, SDUMLA-HMT and THU-FVT2. The results showed that the proposed method produced higher accuracy compared to other state-of-the-art features of finger vein recognition biometric method. INDEX TERMSDeep learning, finger vein based biometric, canonical correlation analysis, principal component analysis, PCANet, finger vein recognition.

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“…To assess our methodology, we utilized four publicly available datasets specifically focused on finger veins: SDUMLA-FV [10], FV-USM [11], SCUT-FVD [12], and THU-FVFDT [13]. The detailed properties of these datasets are discussed in the subsequent section.…”

Section: Finger-vein Public Datasetsmentioning

confidence: 99%

“…The efficacy and versatility of Zero-FVeinNet have been rigorously tested across multiple prominent public finger vein datasets, including SDUMLA-FV [10], FV-USM [11] from the University Sains Malaysia, SCUT-FVD [12], and THU-FVFDT [13]. The findings confirm that Zero-FVeinNet not only surpasses conventional methods in speed and efficiency but also sets new benchmarks in biometric identity recognition, promising a new direction for the use of biometric security technology in mobile and embedded systems.…”

mentioning

confidence: 99%

Zero-FVeinNet: Optimizing Finger Vein Recognition with Shallow CNNs and Zero-Shuffle Attention for Low-Computational Devices

Tran,

Pham,

Chu

et al. 2024

Electronics

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In the context of increasing reliance on mobile devices, robust personal security solutions are critical. This paper presents Zero-FVeinNet, an innovative, lightweight convolutional neural network (CNN) tailored for finger vein recognition on mobile and embedded devices, which are typically resource-constrained. The model integrates cutting-edge features such as Zero-Shuffle Coordinate Attention and a blur pool layer, enhancing architectural efficiency and recognition accuracy under various imaging conditions. A notable reduction in computational demands is achieved through an optimized design involving only 0.3 M parameters, thereby enabling faster processing and reduced energy consumption, which is essential for mobile applications. An empirical evaluation on several leading public finger vein datasets demonstrates that Zero-FVeinNet not only outperforms traditional biometric systems in speed and efficiency but also establishes new standards in biometric identity verification. The Zero-FVeinNet achieves a Correct Identification Rate (CIR) of 99.9% on the FV-USM dataset, with a similarly high accuracy on other datasets. This paper underscores the potential of Zero-FVeinNet to significantly enhance security features on mobile devices by merging high accuracy with operational efficiency, paving the way for advanced biometric verification technologies.

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A Database with ROI Extraction for Studying Fusion of Finger Vein and Finger Dorsal Texture

Cited by 12 publications

References 6 publications

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism

A New Filter Generation Method in PCANet for Finger Vein Recognition

Zero-FVeinNet: Optimizing Finger Vein Recognition with Shallow CNNs and Zero-Shuffle Attention for Low-Computational Devices

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