Multi-feature fusion partitioned local binary pattern method for finger vein recognition

Zhang, Zhongxia; Wang, Mingwen

doi:10.1007/s11760-021-02058-2

Cited by 20 publications

(9 citation statements)

References 31 publications

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“…In our previous study [ 30 ], we found that for a given image size, the higher the number of blocks, the higher the recognition rate, and when the number of blocks reaches a certain number, the recognition rate does not decrease. Based on this, we mainly explore the effect of the number of blocks on the performance of the proposed scheme in this section.…”

Section: Experiments and Experimental Resultsmentioning

confidence: 98%

“…These methods include local binary mode (LBP) [28] and local derivative mode (LDP) [29]. Many LBP variants have also been proposed [30]. Zhang et al [31] presented directional binary code, which is a new LBP variant.…”

Section: Local Features Methodsmentioning

confidence: 99%

“…These methods include local binary mode (LBP) [ 28 ] and local derivative mode (LDP) [ 29 ]. Many LBP variants have also been proposed [ 30 ]. Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

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A Simple and Efficient Method for Finger Vein Recognition

Zhang

Wang

2022

Sensors

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Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishing ability, security, and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tackle these problems, this paper proposes a new finger vein recognition algorithm by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate, and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then, the centrosymmetric coding was performed using the matrix generated by blocking and averaging. The obtained codewords were concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-the-art methods and has competitive potential in performing the matching task.

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Section: Experiments and Experimental Resultsmentioning

confidence: 98%

Section: Local Features Methodsmentioning

confidence: 99%

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A Simple and Efficient Method for Finger Vein Recognition

Zhang

Wang

2022

Sensors

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“…Local features methods are widely used in finger vein recognition, classical local features methods include the local binary pattern (LBP) [16] and the local derivative pattern (LDP) [17]. On the basis of LBP and LDP, researchers have also proposed a number of variants [18]. Zhang et al [19] proposed the directional binary code, which is a new variant of LBP.…”

Section: A Related Workmentioning

confidence: 99%

An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

et al. 2022

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As people's daily behavioral activities become more data-based, how to protect personal information security is a key consideration for the whole society. Finger vein recognition is becoming an important means of identification because of its uniqueness, live detection, security and many other advantages. Although deep learning can make finger vein recognition have good effect, but the number of samples needed to build deep network model is too large, and the current authoritative finger vein database cannot reach the minimum number of samples required. The emergence of Muti-Grained Cascade Forest provides a solution to the problem of insufficient sample data and long training time, which can give a new research avenue in feature extraction. In order to obtain higher accuracy, the deep forest algorithm is introduced in this paper to process the finger vein images. Firstly, the image data in the finger vein image database is pre-processed to prepare for the subsequent feature extraction and matching. Then, the deep forest algorithm is used to find the feature points and ORB algorithm is used to match the features to obtain the angular information of each matched pair, and the final identity is determined according to the sparse distribution of angles. The accuracy of finger vein recognition based on deep forest algorithm is 98.40%. By comparing with other machine learning methods for finger vein recognition, the proposed method has a higher accuracy rate.

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“…In last two decades local descriptors [1][2][3] have shown significant improvements with respect to the application they were developed. Local descriptors have gone from strength to strength during the passing years.…”

Section: Introductionmentioning

confidence: 99%

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

Karanwal

Diwakar

2023

JGEU

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LBP and majority of its variants performs extremely well in front of moderate light variations. But when light variations becomes severe then performance of LBP and its variants is not satisfactory. Therefore there is a need of the more promising and impressive descriptor which performs well in harsh light variations. To complement these LBP based descriptors the proposed work launches the novel descriptor for Face Recognition (FR) in harsh lightning variations. This proposed descriptor is called as Radial Orthogonal Median LBP (ROM-LBP). The main demerit of these LBP based descriptors is that they all consider the uniform coordination between the neighbors and center pixel. Which mean raw pixel intensity is used for the comparison with the center pixel. The proposed work eliminates this problem in the introduced descriptor ROM-LBP, by replacing the raw pixels intensity with the median of the radial points in each orthogonal position of the two separate groups. The generated median is then used for comparison with the center pixel. The respective codes obtained from both the groups are concatenated to form the ROM-LBP size. As region feature extraction is done therefore ROM-LBP develops the large feature size. To make more effective descriptor, the services of FLDA is used and then classification was conducted by SVMs. Experiments conducted on EYB and YB datasets demonstrates the ability of the proposed ROM-LBP against various LBP and non-LBP based descriptors.

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Multi-feature fusion partitioned local binary pattern method for finger vein recognition

Cited by 20 publications

References 31 publications

A Simple and Efficient Method for Finger Vein Recognition

A Simple and Efficient Method for Finger Vein Recognition

An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

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