DANNET: deep attention neural network for efficient ear identification in biometrics
Biometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially ob...
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| Format: | Article |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2603.pdf |
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| author | Deepthy Mary Alex Kalpana Chowdary M. Hanan Abdullah Mengash Venkata Dasu M. Natalia Kryvinska Chinna Babu J. Ajmeera Kiran |
| author_facet | Deepthy Mary Alex Kalpana Chowdary M. Hanan Abdullah Mengash Venkata Dasu M. Natalia Kryvinska Chinna Babu J. Ajmeera Kiran |
| author_sort | Deepthy Mary Alex |
| collection | DOAJ |
| description | Biometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. Despite numerous proposed convolutional neural network (CNN) based deep learning techniques for ear detection, achieving the expected efficiency and accuracy remains a challenge. In this manuscript, we propose a sophisticated method for ear biometric identification, named the encoder-decoder deep learning ensemble technique incorporating attention blocks. This innovative approach leverages the strengths of encoder-decoder architectures and attention mechanisms to enhance the precision and reliability of ear detection and segmentation. Specifically, our method employs an ensemble of two YSegNets, which significantly improves the performance over a single YSegNet. The use of an ensemble method is crucial in ear biometrics due to the variability and complexity of ear shapes and the potential for partial occlusions. By combining the outputs of two YSegNets, our approach can capture a wider range of features and reduce the risk of false positives and negatives, leading to more robust and accurate segmentation results. Experimental validation of the proposed method was conducted using a combination of data from the EarVN1.0, AMI, and Human Face datasets. The results demonstrate the effectiveness of our approach, achieving a segmentation framework accuracy of 98.93%. This high level of accuracy underscores the potential of our method for practical applications in biometric identification. The proposed innovative method demonstrates significant potential for individual recognition, particularly in scenarios involving large gatherings. When complemented by an effective surveillance system, our method can contribute to improved security and identification processes in public spaces. This research not only advances the field of ear biometrics but also provides a viable solution for biometric identification in the context of mask-wearing and other facial obstructions. |
| format | Article |
| id | doaj-art-1983689cbbaa4147b3a513be4df0e03e |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-1983689cbbaa4147b3a513be4df0e03e2025-08-20T01:57:00ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e260310.7717/peerj-cs.2603DANNET: deep attention neural network for efficient ear identification in biometricsDeepthy Mary Alex0Kalpana Chowdary M.1Hanan Abdullah Mengash2Venkata Dasu M.3Natalia Kryvinska4Chinna Babu J.5Ajmeera Kiran6Department of Electronics and Communication Engineering, Mangalam College of Engineering, Ettumanoor, Kerala, IndiaDepartment of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, IndiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Electronics and Communication Engineering, Annamacharya University, Rajampet, Andhra Pradesh, IndiaDepartment of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava, Bratislava, SlovakiaDepartment of Electronics and Communication Engineering, Annamacharya University, Rajampet, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, IndiaBiometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. Despite numerous proposed convolutional neural network (CNN) based deep learning techniques for ear detection, achieving the expected efficiency and accuracy remains a challenge. In this manuscript, we propose a sophisticated method for ear biometric identification, named the encoder-decoder deep learning ensemble technique incorporating attention blocks. This innovative approach leverages the strengths of encoder-decoder architectures and attention mechanisms to enhance the precision and reliability of ear detection and segmentation. Specifically, our method employs an ensemble of two YSegNets, which significantly improves the performance over a single YSegNet. The use of an ensemble method is crucial in ear biometrics due to the variability and complexity of ear shapes and the potential for partial occlusions. By combining the outputs of two YSegNets, our approach can capture a wider range of features and reduce the risk of false positives and negatives, leading to more robust and accurate segmentation results. Experimental validation of the proposed method was conducted using a combination of data from the EarVN1.0, AMI, and Human Face datasets. The results demonstrate the effectiveness of our approach, achieving a segmentation framework accuracy of 98.93%. This high level of accuracy underscores the potential of our method for practical applications in biometric identification. The proposed innovative method demonstrates significant potential for individual recognition, particularly in scenarios involving large gatherings. When complemented by an effective surveillance system, our method can contribute to improved security and identification processes in public spaces. This research not only advances the field of ear biometrics but also provides a viable solution for biometric identification in the context of mask-wearing and other facial obstructions.https://peerj.com/articles/cs-2603.pdfEar biometricsDeep learningSegmentationYSegNetEnsembleUNet |
| spellingShingle | Deepthy Mary Alex Kalpana Chowdary M. Hanan Abdullah Mengash Venkata Dasu M. Natalia Kryvinska Chinna Babu J. Ajmeera Kiran DANNET: deep attention neural network for efficient ear identification in biometrics PeerJ Computer Science Ear biometrics Deep learning Segmentation YSegNet Ensemble UNet |
| title | DANNET: deep attention neural network for efficient ear identification in biometrics |
| title_full | DANNET: deep attention neural network for efficient ear identification in biometrics |
| title_fullStr | DANNET: deep attention neural network for efficient ear identification in biometrics |
| title_full_unstemmed | DANNET: deep attention neural network for efficient ear identification in biometrics |
| title_short | DANNET: deep attention neural network for efficient ear identification in biometrics |
| title_sort | dannet deep attention neural network for efficient ear identification in biometrics |
| topic | Ear biometrics Deep learning Segmentation YSegNet Ensemble UNet |
| url | https://peerj.com/articles/cs-2603.pdf |
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