Accurate wild animal recognition using PCA, LDA and LBPH

Abstract

In this paper, the performances of image recognition methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns Histograms (LBPH) are tested and compared for the image recognition of the input animal images. The main idea of this paper is to present an independent, comparative study and some of the benefits and drawbacks of these most popular image recognition methods. Two sets of experiments are conducted for relative performance evaluations. In the first part of our experiments, the recognition accuracy of PCA, LDA and LBPH is demonstrated. The overall time execution for animal recognition process is evaluated in the second set of our experiments. We conduct tests on created animal database. The all algorithms have been tested on 300 different subjects (60 images for each class). The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set.