A biometric-based model for fish species classification

Abstract Fish identification is crucial for the survival of our threatened fish species. In this paper, a novel and robust biometric-based approach was proposed to identify fish species. The proposed approach consists of three phases. In the first phase, different features were extracted from fish images. In this phase, Weber's Local Descriptor (WLD) and color moments were used to extract texture and color features, respectively. Due to the high dimensionality of WLD features, in the second phase, Linear Discriminant Analysis (LDA) was applied to reduce the number of features and to discriminate between different classes. In the third phase, the AdaBoost classifier was used to identify fish species. We have collected a dataset that consists of four classes/species. To validate the results of the AdaBoost classifier, a comparison between three well-known classifiers (Naive Bayesian, k -Nearest Neighbor, and Multilayer Perceptron) was performed. The experimental results proved that our approach achieved excellent results (approximately 96.4%). Moreover, our model has been tested against different real challenges such as image rotation and image translation, and the proposed model achieved promising results.