Forearm multimodal recognition based on IAHP‐entropy weight combination
Abstract Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID‐19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recogn...
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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 |
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| Online Access: | https://doi.org/10.1049/bme2.12080 |
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| _version_ | 1832559569795022848 |
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| author | Chaoying Tang Mengen Qian Ru Jia Haodong Liu Biao Wang |
| author_facet | Chaoying Tang Mengen Qian Ru Jia Haodong Liu Biao Wang |
| author_sort | Chaoying Tang |
| collection | DOAJ |
| description | Abstract Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID‐19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near‐Infrared (Near‐Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process‐entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance. |
| format | Article |
| id | doaj-art-9cf6bd4c738e48fb8124e0b1e09a4dfa |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-9cf6bd4c738e48fb8124e0b1e09a4dfa2025-02-03T01:29:43ZengWileyIET Biometrics2047-49382047-49462023-01-01121526310.1049/bme2.12080Forearm multimodal recognition based on IAHP‐entropy weight combinationChaoying Tang0Mengen Qian1Ru Jia2Haodong Liu3Biao Wang4College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing ChinaCollege of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing ChinaCollege of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing ChinaCollege of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing ChinaCollege of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing ChinaAbstract Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID‐19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near‐Infrared (Near‐Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process‐entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance.https://doi.org/10.1049/bme2.12080graph theorymultibiometricssoft biometricsvein recognition |
| spellingShingle | Chaoying Tang Mengen Qian Ru Jia Haodong Liu Biao Wang Forearm multimodal recognition based on IAHP‐entropy weight combination IET Biometrics graph theory multibiometrics soft biometrics vein recognition |
| title | Forearm multimodal recognition based on IAHP‐entropy weight combination |
| title_full | Forearm multimodal recognition based on IAHP‐entropy weight combination |
| title_fullStr | Forearm multimodal recognition based on IAHP‐entropy weight combination |
| title_full_unstemmed | Forearm multimodal recognition based on IAHP‐entropy weight combination |
| title_short | Forearm multimodal recognition based on IAHP‐entropy weight combination |
| title_sort | forearm multimodal recognition based on iahp entropy weight combination |
| topic | graph theory multibiometrics soft biometrics vein recognition |
| url | https://doi.org/10.1049/bme2.12080 |
| work_keys_str_mv | AT chaoyingtang forearmmultimodalrecognitionbasedoniahpentropyweightcombination AT mengenqian forearmmultimodalrecognitionbasedoniahpentropyweightcombination AT rujia forearmmultimodalrecognitionbasedoniahpentropyweightcombination AT haodongliu forearmmultimodalrecognitionbasedoniahpentropyweightcombination AT biaowang forearmmultimodalrecognitionbasedoniahpentropyweightcombination |