| Issue |
MATEC Web Conf.
Volume 277, 2019
2018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|
|
|---|---|---|
| Article Number | 02033 | |
| Number of page(s) | 5 | |
| Section | Data and Signal Processing | |
| DOI | https://doi.org/10.1051/matecconf/201927702033 | |
| Published online | 02 April 2019 | |
Animal species classification using machine learning techniques
Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo, Japan
* Corresponding author: mg17003@shibaura-it.ac.jp
Animals recognition is one of the research areas in which few effective technologies have been proposed, especially in the predator animals' domain. Predator animals present a great danger to people who are camping or staying in outdoor areas and they are also a menace to livestock. In this paper, a multiple feature detection of predator animals is proposed. This method focuses on the face of the animal, explicitly the eyes and the ears. A database was created by collecting the features of ears and eyes from 10 animals and an experiment was conducted using machine learning techniques such as SVM and MLP to classify them as predators or pets. The evaluation results achieved the classification accuracies of 82% for MLP and 78% for SVM, which justify its effectiveness for the proposed method.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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