A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics
Structural features are capable of effectively capturing the overall texture variations in images. However, in locally prominent areas with visible veins, other characteristics such as directionality, convexity–concavity, and curvature also play a crucial role in recognition, and their impact cannot...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2024/7408331 |
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| author | Min Li Xue Jiang Honghao Zhu Fei Liu Huabin Wang Liang Tao Shijun Liu |
| author_facet | Min Li Xue Jiang Honghao Zhu Fei Liu Huabin Wang Liang Tao Shijun Liu |
| author_sort | Min Li |
| collection | DOAJ |
| description | Structural features are capable of effectively capturing the overall texture variations in images. However, in locally prominent areas with visible veins, other characteristics such as directionality, convexity–concavity, and curvature also play a crucial role in recognition, and their impact cannot be overlooked. This paper introduces a novel approach, the histogram of variable curvature directional binary statistical (HVCDBS), which combines the structural and directional features of images. The proposed method is designed for extracting discriminative multifeature information in vein recognition. First, a multidirection and multicurvature Gabor filter is introduced for convolution with vein images, yielding directional and convexity–concavity information at each pixel, along with curvature information for the corresponding curve. Simultaneously incorporating the original image feature information, these four aspects of information are fused and encoded to construct a variable curvature binary pattern (VCBP) with multifeatures. Second, the feature map containing multifeature information is blockwise processed to build variable curvature binary statistical features. Finally, competitive Gabor directional binary statistical features are combined, and a matching score-level fusion scheme is employed based on maximizing the interclass distance and minimizing the intraclass distance to determine the optimal weights. This process fuses the two feature maps into a one-dimensional feature vector, achieving an effective representation of vein images. Extensive experiments were conducted on four widely utilized vein databases, and the results indicate that the proposed algorithm, compared with solely extraction of structural features, achieved higher recognition rates and lower equal error rates. |
| format | Article |
| id | doaj-art-0a9178388e4e497cb9b4d37b3daac1ef |
| institution | Kabale University |
| issn | 2047-4946 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-0a9178388e4e497cb9b4d37b3daac1ef2025-02-02T23:15:36ZengWileyIET Biometrics2047-49462024-01-01202410.1049/2024/7408331A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary StatisticsMin Li0Xue Jiang1Honghao Zhu2Fei Liu3Huabin Wang4Liang Tao5Shijun Liu6School of Computer and Information EngineeringSchool of Computer and Information EngineeringSchool of Computer and Information EngineeringSchool of EngineeringSchool of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer and Information EngineeringStructural features are capable of effectively capturing the overall texture variations in images. However, in locally prominent areas with visible veins, other characteristics such as directionality, convexity–concavity, and curvature also play a crucial role in recognition, and their impact cannot be overlooked. This paper introduces a novel approach, the histogram of variable curvature directional binary statistical (HVCDBS), which combines the structural and directional features of images. The proposed method is designed for extracting discriminative multifeature information in vein recognition. First, a multidirection and multicurvature Gabor filter is introduced for convolution with vein images, yielding directional and convexity–concavity information at each pixel, along with curvature information for the corresponding curve. Simultaneously incorporating the original image feature information, these four aspects of information are fused and encoded to construct a variable curvature binary pattern (VCBP) with multifeatures. Second, the feature map containing multifeature information is blockwise processed to build variable curvature binary statistical features. Finally, competitive Gabor directional binary statistical features are combined, and a matching score-level fusion scheme is employed based on maximizing the interclass distance and minimizing the intraclass distance to determine the optimal weights. This process fuses the two feature maps into a one-dimensional feature vector, achieving an effective representation of vein images. Extensive experiments were conducted on four widely utilized vein databases, and the results indicate that the proposed algorithm, compared with solely extraction of structural features, achieved higher recognition rates and lower equal error rates.http://dx.doi.org/10.1049/2024/7408331 |
| spellingShingle | Min Li Xue Jiang Honghao Zhu Fei Liu Huabin Wang Liang Tao Shijun Liu A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics IET Biometrics |
| title | A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics |
| title_full | A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics |
| title_fullStr | A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics |
| title_full_unstemmed | A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics |
| title_short | A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics |
| title_sort | finger vein recognition algorithm based on the histogram of variable curvature directional binary statistics |
| url | http://dx.doi.org/10.1049/2024/7408331 |
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