Animal Recognition System Based on Convolutional Neural Network
Tibor Trnovszky, Patrik Kamencay, Richard Orjesek, Miroslav Benco, Peter Sykora
DOI: 10.15598/aeee.v15i3.2202
Abstract
In this paper, the Convolutional Neural Network (CNN) for the classification of the input animal images is proposed. This method is compared with well-known image recognition methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Patterns Histograms (LBPH) and Support Vector Machine (SVM). The main goal is to compare the overall recognition accuracy of the PCA, LDA, LBPH and SVM with proposed CNN method. For the experiments, the database of wild animals is created. This database consists of 500 different subjects (5 classes / 100 images for each class). The overall performances were obtained using different number of training images and test images. The experimental results show that the proposed method has a positive effect on overall animal recognition performance and outperforms other examined methods.