Customized Local Line Binary Pattern Method for Finger Vein Recognition

Liu, Haiying; Song, Lingfei; Yang, Geng; Yang, Gongping; Yin, Yilong

doi:10.1007/978-3-319-69923-3_34

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…Moreover, in terms of equal error rate, conventional finger vein technique also achieved some tremendous achievements. The method proposed in References [77,78] brings about very low equal error rate of 0.61 and 0.055 on 100 and 156 subject's databases respectively. Most of the conventional finger vein recognition techniques show remarkable performance in terms of accuracy and equal error rate; however the total computational cost of the conventional finger vein algorithm is much too high [73,74,77].…”

Section: Conventional Finger Vein Recognition Methodsmentioning

confidence: 98%

“…However, LLBP and PLLBP have low discriminatory information and a jumble of redundant information. Hence, Liu et al advance a novel customized local line binary pattern (CLLBP) [78] approach to eliminate the information reduction, increase discriminatory information of local features and reduce the matching time of recognition system. Extraction of powerful feature greatly improves the performance of finger vein identification.…”

Section: Local Binary-based (Lbp) Methodsmentioning

confidence: 99%

“…Conventional finger vein approaches were less robust to noise and misalignment than machine learning approaches; therefore, image preprocessing methods were usually applied ahead of feature extraction and matching to overcome the above-mentioned problems. Numerous conventional finger vein identification methods have been developed, but some methods [73,76,78,83] have achieved remarkable development. In 2010, Lie et al [83] proposed a finger vein verification approach, and obtained accuracy of 97.8% for identification.…”

Section: Conventional Finger Vein Recognition Methodsmentioning

confidence: 99%

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A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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Section: Conventional Finger Vein Recognition Methodsmentioning

confidence: 98%

Section: Local Binary-based (Lbp) Methodsmentioning

confidence: 99%

Section: Conventional Finger Vein Recognition Methodsmentioning

confidence: 99%

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A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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“…They achieved an Identification Recognition Rate (IRR) of 99.21 using SDUMLA-HMT database. Later in 2017, [22] introduced a new finger-vein system that was based on Customized Local Line Binary Pattern (CLLBP) which considers the coding in eight directions. Liu and others' system achieved EER of 0.055% using Hong Kong Polytechnic University (HKPU) database.…”

Section: Related Workmentioning

confidence: 99%

A New Finger-Vein Recognition System Using the Complete Local Binary Pattern and the Phase Only Correlation

Mustafa¹,

Tahir²

2021

IJASIS

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A new system for finger-vein recognition is proposed based on the Complete Local Binary pattern (CLBP) as a feature extractor and the Phase Only Correlation (POC) for post-processing alignment and for speeding up the system. The CLBP produces three components of image descriptors and thus holds more details compared to the previous methods such as the Local Binary Pattern (LBP), the Local Directional Pattern (LDP), the Local Line Binary Pattern (LLBP), the Repeated Line Tracking (RLT), the Maximum Curvature (MC) and the Wide Line Detector (WLD). In the proposed system, POC is used for two purposes. First, to increase the performance of the system the alignment between the CLBP components of the test image and the enrolled CLBP components are performed. Second, to speed up the matching stage, a portion of the enrolled images is excluded that are highly misaligned with the test image from the Hamming Distance (HD) measure competition in the matching stage. To make the system more secure against attacks targeting personal information, only CLBP components are enrolled in the system and the alignment process POC is implemented on these components without the need to original images. For image pre-processing a novel scheme of preprocessing methods is adopted including finger-vein localization, alignment, and the Region-Of-Interest (ROI) extraction and enhancement. Two databases, UTFVP and SDUMLA-HMT, are used to evaluate the performance of the system. The results have shown that the values for the Identification Recognition Rate (IRR) and the Equal Error Rate (EER) are respectively (99.66%) and (0.139) for the UTFVP database and (98.95%, and 0.53%) for SDUMLA-HMT database. These results are competitive compared to those achieved by the state-of-art systems.

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“…The existing handcrafted feature-based methods for fingervein recognition, such as local binary pattern (LBP) LBP [8], [9], [17], [19], [20], [22], [23] and local directional code (LDC) [16], have reduced recognition accuracy if noise caused by shades and changes in illumination in images are not properly corrected during preprocessing or fingervein lines are not enhanced by applying optimal filters [4], [6], [11]; moreover, the optimal filters need to be manually calculated to be customized for each database.…”

Section: Introductionmentioning

confidence: 99%

Finger-Vein Recognition Based on Densely Connected Convolutional Network Using Score-Level Fusion With Shape and Texture Images

et al. 2020

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Biometrics using finger-veins is a recognition method based on the shape of veins in fingers, and it has the advantage of difficulty to be forged. However, a shade is inevitably produced due to the bones and fingernails, and a change in illumination occurs when acquiring the images of finger-veins. Previous studies have conducted finger-vein recognition using a single-type texture image or finger-vein segmented image (shape image). A texture image provides numerous features, but it is vulnerable to the changes in illumination during recognition and contains noises in regions other than the finger-vein region. A shape image is less affected by noises; however, the recognition accuracy is significantly reduced due to fewer features available and mis-segmented regions caused by shades. In this study, therefore, rough finger-vein regions in an image are detected to reduce the effect of mis-segmented regions, to complement the drawbacks of shape image-based finger-vein recognition. Furthermore, score-level fusion is performed for two output scores of deep convolutional neural network extracted from the texture and shape images, which can reduce the sensitivity to noise, while diverse features provided in the texture image are used efficiently. Two open databases, the Shandong University homologous multi-modal traits finger-vein database and Hong Kong Polytech University finger image database, are used for experiments, and the proposed method shows better recognition performance than the state-of-the-art method.INDEX TERMS Finger-vein recognition, shape and texture images of finger-vein, deep CNN, score-level fusion.

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Customized Local Line Binary Pattern Method for Finger Vein Recognition

Cited by 9 publications

References 16 publications

A Systematic Review of Finger Vein Recognition Techniques

A Systematic Review of Finger Vein Recognition Techniques

A New Finger-Vein Recognition System Using the Complete Local Binary Pattern and the Phase Only Correlation

Finger-Vein Recognition Based on Densely Connected Convolutional Network Using Score-Level Fusion With Shape and Texture Images

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