Multimedia Tools and Applications

, Volume 76, Issue 24, pp 26551–26580 | Cite as

Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm

Article
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Abstract

Animal biometrics is an emerging research field that develops quantified methodologies for representing and detecting the visual appearances of animal based on generic features and primary biometric characteristics. The identification of individual cattle is an important issue for classification of different breads, their registration, traceability, health management, and verification of false insurance claim throughout the world. To solve these major problems, the muzzle (nose) point image pattern of cattle is a suitable and primary biometric characteristic for the recognition of cattle. The recognition of muzzle point images is similar to the recognition of minutiae points in the human fingerprints. In this paper, we propose a hybrid feature extraction approach for the automatic recognition and classification of cattle breeds based on captured muzzle point image pattern features using low- cost camera. The major contributions of this research is based on following aspects: (1) preparation of muzzle point image dataset, (2) extraction of salient set of features and (3) K-nearest neighbour (K-NN), Fuzzy-KNN, Decision Tree (DT), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN), Multilayer Perceptron (MLP) and Naive Bayes classification models are applied to recognition, and classification of individual cattle using their muzzle point pattern. This paper, therefore, demonstrates the automatic recognition and classification of livestock using the set of extracted muzzle point image features. The experimental results show that proposed hybrid feature extraction and recognition approach outperforms the current state-of- the art method for the identification of individual cattle using their muzzle point image pattern.

Keywords

Animal biometrics Muzzle point pattern Cattle recognition Feature extraction Classification Texture feature 
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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (Banaras Hindu University)VaranasiIndia

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