Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model
Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/22/8988 |
| _version_ | 1827643649293287424 |
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| author | Md Mursalin Mohiuddin Ahmed Paul Haskell-Dowland |
| author_facet | Md Mursalin Mohiuddin Ahmed Paul Haskell-Dowland |
| author_sort | Md Mursalin |
| collection | DOAJ |
| description | Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application. |
| first_indexed | 2024-03-09T18:00:05Z |
| format | Article |
| id | doaj.art-7195871b46ad438c98444109c5e8401a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:00:05Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7195871b46ad438c98444109c5e8401a2023-11-24T09:58:59ZengMDPI AGSensors1424-82202022-11-012222898810.3390/s22228988Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear ModelMd Mursalin0Mohiuddin Ahmed1Paul Haskell-Dowland2School of Science, Edith Cowan University, Perth 6027, AustraliaSchool of Science, Edith Cowan University, Perth 6027, AustraliaSchool of Science, Edith Cowan University, Perth 6027, AustraliaBiometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.https://www.mdpi.com/1424-8220/22/22/8988ear biometricsdetection3D morphable modelrecognition |
| spellingShingle | Md Mursalin Mohiuddin Ahmed Paul Haskell-Dowland Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model Sensors ear biometrics detection 3D morphable model recognition |
| title | Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model |
| title_full | Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model |
| title_fullStr | Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model |
| title_full_unstemmed | Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model |
| title_short | Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model |
| title_sort | biometric security a novel ear recognition approach using a 3d morphable ear model |
| topic | ear biometrics detection 3D morphable model recognition |
| url | https://www.mdpi.com/1424-8220/22/22/8988 |
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