Efficient ear alignment using a two‐stack hourglass network
Abstract Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem...
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12109 |
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| _version_ | 1832559570696798208 |
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| author | Anja Hrovatič Peter Peer Vitomir Štruc Žiga Emeršič |
| author_facet | Anja Hrovatič Peter Peer Vitomir Štruc Žiga Emeršič |
| author_sort | Anja Hrovatič |
| collection | DOAJ |
| description | Abstract Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem of ear alignment is still under‐explored in the open literature. Nonetheless, accurate alignment of ear images, especially in unconstrained acquisition scenarios, where the ear appearance is expected to vary widely due to pose and view point variations, is critical for the performance of all downstream tasks, including ear recognition. Here, the authors address this problem and present a framework for ear alignment that relies on a two‐step procedure: (i) automatic landmark detection and (ii) fiducial point alignment. For the first (landmark detection) step, the authors implement and train a Two‐Stack Hourglass model (2‐SHGNet) capable of accurately predicting 55 landmarks on diverse ear images captured in uncontrolled conditions. For the second (alignment) step, the authors use the Random Sample Consensus (RANSAC) algorithm to align the estimated landmark/fiducial points with a pre‐defined ear shape (i.e. a collection of average ear landmark positions). The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2‐SHGNet model leads to more accurate landmark predictions than competing state‐of‐the‐art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery. |
| format | Article |
| id | doaj-art-9e56a5eac85b44dfae3ae810585f097f |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-9e56a5eac85b44dfae3ae810585f097f2025-02-03T01:29:43ZengWileyIET Biometrics2047-49382047-49462023-03-01122779010.1049/bme2.12109Efficient ear alignment using a two‐stack hourglass networkAnja Hrovatič0Peter Peer1Vitomir Štruc2Žiga Emeršič3Computer Vision Lab Faculty of Computer and Information Science University of Ljubljana Ljubljana SloveniaComputer Vision Lab Faculty of Computer and Information Science University of Ljubljana Ljubljana SloveniaLaboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Ljubljana SloveniaComputer Vision Lab Faculty of Computer and Information Science University of Ljubljana Ljubljana SloveniaAbstract Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem of ear alignment is still under‐explored in the open literature. Nonetheless, accurate alignment of ear images, especially in unconstrained acquisition scenarios, where the ear appearance is expected to vary widely due to pose and view point variations, is critical for the performance of all downstream tasks, including ear recognition. Here, the authors address this problem and present a framework for ear alignment that relies on a two‐step procedure: (i) automatic landmark detection and (ii) fiducial point alignment. For the first (landmark detection) step, the authors implement and train a Two‐Stack Hourglass model (2‐SHGNet) capable of accurately predicting 55 landmarks on diverse ear images captured in uncontrolled conditions. For the second (alignment) step, the authors use the Random Sample Consensus (RANSAC) algorithm to align the estimated landmark/fiducial points with a pre‐defined ear shape (i.e. a collection of average ear landmark positions). The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2‐SHGNet model leads to more accurate landmark predictions than competing state‐of‐the‐art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery.https://doi.org/10.1049/bme2.12109convolutional neural netsear biometrics |
| spellingShingle | Anja Hrovatič Peter Peer Vitomir Štruc Žiga Emeršič Efficient ear alignment using a two‐stack hourglass network IET Biometrics convolutional neural nets ear biometrics |
| title | Efficient ear alignment using a two‐stack hourglass network |
| title_full | Efficient ear alignment using a two‐stack hourglass network |
| title_fullStr | Efficient ear alignment using a two‐stack hourglass network |
| title_full_unstemmed | Efficient ear alignment using a two‐stack hourglass network |
| title_short | Efficient ear alignment using a two‐stack hourglass network |
| title_sort | efficient ear alignment using a two stack hourglass network |
| topic | convolutional neural nets ear biometrics |
| url | https://doi.org/10.1049/bme2.12109 |
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