Abstract
This work presents the application of biometrics in animal identification, which is a highly researched topic in human recognition. Here, our analysis presents the identification of zebra in their natural habitat. All the techniques are tested on 824 Plains zebra images captured at Ol’Pejeta conservancy in Laikipia, Kenya. We have used coat strips as a biometric identifier which is unique in nature. To improve the performance of identification, information fusion of coat strips can be taken place from many points in zebra skin such as near legs, stomach and neck. Here two region near stomach (flake) and first limb (leg) is cropped from the textural pattern of strips of zebra is used in feature extraction. GMF, AAD, mean, and eigenface feature extraction methods are applied on flake and limb ROI of zebra. Then a novel image enhancement method: difference subplane adaptive histogram equalization is applied to improve the identification rate. Our technique is based on information fusion in fusing the score from stomach (flake) and first limb (leg) region. For this, sum, product, frank T-norm, and Hamacher T-norm rules are applied to validate the identification results. Information fusion improves the identification results from the previous reported results from eigenface, CO-1 algorithm, and stripecodes. The improvement in results verifies the success of our approach of information fusion using score level fusion.
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Chaudhary, G., Srivastava, S., Bhardwaj, S., Srivastava, S. (2017). Information Fusion in Animal Biometric Identification. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_60
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DOI: https://doi.org/10.1007/978-981-10-3153-3_60
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