Iris and periocular recognition in arabian race horses using deep convolutional neural networks
- Mateusz Trokielewicz,
- Mateusz Szadkowski
This paper presents a study devoted to recognizing horses by means of their iris and periocular features using deep convolutional neural networks (DCNNs). Identification of race horses is crucial for animal identity confirmation prior to racing. As this is usually done shortly before a race, fast and reliable methods that are friendly and inflict no harm upon animals are important. Iris recognition has been shown to work with horse irides, provided that algorithms deployed for such task are fine-tuned for horse irides and input data is of very high quality. In our work, we examine a possibility of utilizing deep convolutional neural networks for a fusion of both iris and periocular region features. With such methodology, ocular biometrics in horses could perform well without employing complicated algorithms that require a lot offline-tuning and prior knowledge of the input image, while at the same time being rotation, translation, and to some extent also image quality invariant. We were able to achieve promising results, with EER=9.5%o using two network architectures with score-level fusion.
- Record ID
- Publication size in sheets
- 2017 IEEE International Joint Conference on Biometrics (IJCB), 2017, IEEE, ISBN 978-1-5386-1124-1
- DOI:10.1109/BTAS.2017.8272736 Opening in a new tab
- (en) English
- Score (nominal)
- Score source
- = 5.0, 25-04-2021, ChapterFromConference
- Publication indicators
- = 4
- Uniform Resource Identifier
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.