UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
The advantages of the ears as a means of identification over other biometric modalities provided an avenue for researchers to conduct biometric recognition studies on state-of-the-art computing methods. This paper presented a deep learning pipeline for unconstrained ear recognition using a Transform...
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| Formato: | article |
| Lenguaje: | EN |
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Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/2d69ca20c56f4b8fae1155a925ccf202 |
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| Sumario: | The advantages of the ears as a means of identification over other biometric modalities provided an avenue for researchers to conduct biometric recognition studies on state-of-the-art computing methods. This paper presented a deep learning pipeline for unconstrained ear recognition using a Transformer neural network: Vision Transformer (ViT) and Data-efficient image Transformers (DeiT). The ViT-Ear and DeiT-Ear models of this study achieved recognition accuracy comparable or more significant than the results of state-of-the-art CNN-based methods and other deep learning algorithms. This study also determined that the performance of Vision Transformer and Data-efficient image Transformer models work better than ResNets without using exhaustive data augmentation processes. Moreover, this study observed that the performance of ViT-Ear is nearly similar to other ViT-based biometric studies. [JJCIT 2021; 7(4.000): 326-336] |
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