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Deep Learning Framework for Recognition of Cattle Using Muzzle Point Image Pattern

Abstract

Recently, deep learning approaches have achieved more attention for recognition of species or individual animal using visual features. In this chapter, the deep learning-based recognition system is proposed for identification of different cattle based on their primary muzzle point (nose pattern) image pattern characteristics to solve major problem of missed or swapped animal and false insurance claims. The major contributions of the research work are as follows: (1) preparation of muzzle point image database, which are not publically available; (2) extraction of the salient set of texture features and representation of muzzle point image of cattle using the deep learning-based convolution neural network and deep belief neural network proposed approaches. The stacked denoising auto-encoder technique is applied to encode the extracted feature of muzzle point images; and (3) experimental results and analysis of proposed approach. Extensive experimental results illustrate that the proposed deep learning approach outperforms state-of-the-art methods for recognition of cattle on muzzle point image database. The efficacy of the proposed deep learning approach is computed under different identification settings. With multiple test galleries, rank-1 identification accuracy of 98.99% is achieved.

Keywords

  • Cattle recognition
  • Muzzle point image
  • Deep learning
  • Convolution neural network
  • DBN
  • SDAE
  • Verification
  • Computer vision
  • LBP
  • SURF
  • PCA
  • VLAD
  • LDA

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Kumar, S., Singh, S.K., Singh, R., Singh, A.K. (2017). Deep Learning Framework for Recognition of Cattle Using Muzzle Point Image Pattern. In: Animal Biometrics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7956-6_6

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  • DOI: https://doi.org/10.1007/978-981-10-7956-6_6

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