| dc.contributor.author |
Bera, Asish |
|
| dc.date.accessioned |
2024-10-18T10:57:09Z |
|
| dc.date.available |
2024-10-18T10:57:09Z |
|
| dc.date.issued |
2023-07 |
|
| dc.identifier.uri |
https://link.springer.com/chapter/10.1007/978-981-99-2710-4_42 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16133 |
|
| dc.description.abstract |
Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Springer |
en_US |
| dc.subject |
Computer Science |
en_US |
| dc.subject |
Convolutional Neural Networks (CNN) |
en_US |
| dc.subject |
Biometrics |
en_US |
| dc.title |
Deep Ear Biometrics for Gender Classification |
en_US |
| dc.type |
Article |
en_US |