DANNET: deep attention neural network for efficient ear identification in biometrics Article Swipe
Deepthy Mary Alex
,
Kurniawan Teguh Martono
,
Hanan Abdullah Mengash
,
Venkata Dasu M.
,
Natalia Kryvinska
,
J. Chinna Babu
,
Ajmeera Kiran
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.2603
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.2603
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.
Related Topics
Concepts
Biometrics
Computer science
Artificial intelligence
Convolutional neural network
Segmentation
Identification (biology)
False positive paradox
Deep learning
Pattern recognition (psychology)
Face (sociological concept)
Encoder
Machine learning
Speech recognition
Social science
Botany
Sociology
Biology
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj-cs.2603
- OA Status
- gold
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405554563
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405554563Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7717/peerj-cs.2603Digital Object Identifier
- Title
-
DANNET: deep attention neural network for efficient ear identification in biometricsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-18Full publication date if available
- Authors
-
Deepthy Mary Alex, Kurniawan Teguh Martono, Hanan Abdullah Mengash, Venkata Dasu M., Natalia Kryvinska, J. Chinna Babu, Ajmeera KiranList of authors in order
- Landing page
-
https://doi.org/10.7717/peerj-cs.2603Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7717/peerj-cs.2603Direct OA link when available
- Concepts
-
Biometrics, Computer science, Artificial intelligence, Convolutional neural network, Segmentation, Identification (biology), False positive paradox, Deep learning, Pattern recognition (psychology), Face (sociological concept), Encoder, Machine learning, Speech recognition, Social science, Botany, Sociology, Biology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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