Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition
Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recog...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2023/9253739 |
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| _version_ | 1850110467127640064 |
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| author | Lizhen Zhou Lu Yang Deqian Fu Gongping Yang |
| author_facet | Lizhen Zhou Lu Yang Deqian Fu Gongping Yang |
| author_sort | Lizhen Zhou |
| collection | DOAJ |
| description | Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance. |
| format | Article |
| id | doaj-art-46b4fe75313d42b89707a29a4aa4d4b4 |
| institution | OA Journals |
| issn | 2047-4946 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-46b4fe75313d42b89707a29a4aa4d4b42025-08-20T02:37:49ZengWileyIET Biometrics2047-49462023-01-01202310.1049/2023/9253739Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein RecognitionLizhen Zhou0Lu Yang1Deqian Fu2Gongping Yang3Department of InformationSchool of Computer Science and TechnologySchool of Information Science and EngineeringSchool of SoftwareFinger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance.http://dx.doi.org/10.1049/2023/9253739 |
| spellingShingle | Lizhen Zhou Lu Yang Deqian Fu Gongping Yang Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition IET Biometrics |
| title | Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition |
| title_full | Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition |
| title_fullStr | Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition |
| title_full_unstemmed | Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition |
| title_short | Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition |
| title_sort | encoding coefficient similarity based multifeature sparse representation for finger vein recognition |
| url | http://dx.doi.org/10.1049/2023/9253739 |
| work_keys_str_mv | AT lizhenzhou encodingcoefficientsimilaritybasedmultifeaturesparserepresentationforfingerveinrecognition AT luyang encodingcoefficientsimilaritybasedmultifeaturesparserepresentationforfingerveinrecognition AT deqianfu encodingcoefficientsimilaritybasedmultifeaturesparserepresentationforfingerveinrecognition AT gongpingyang encodingcoefficientsimilaritybasedmultifeaturesparserepresentationforfingerveinrecognition |