Abstract
Animal biometrics-based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This chapter presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust feature and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of identification accuracy. Moreover, the comparative analysis of experimental results for proposed work and appearance-based face recognition algorithms has been done at each level. The proposed work, therefore, can be a potential approach for the recognition of individual cattle using muzzle point image pattern.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Khl, H. S., & Burghardt, T. (2013). Animal biometrics: Quantifying and detecting phenotypic appearance. Trends in Ecology & Evolution, 28(2), 432–441.
Duyck, J., Finn, C., Hutcheon, A., Vera, P., Salas, J., & Ravela, S. (2015). Sloop: A pattern retrieval engine for individual animal identification. Pattern Recognition, 48(4), 1059–1073.
Finn, C., Duyck, J., Hutcheon, A., Vera, P., Salas, J., & Ravela, S. (2014). Relevance feedback in biometric retrieval of animal photographs. In Proceedings of 6th Mexican Conference, MCPR 2014 (pp. 281–290). Cancun, Mexico.
Baranov, A. S., Graml, R., Pirchner, F., & Schmid, D. O. (2014). Breed differences and intrabreed genetic variability of dermatoglyphic pattern of cattle. Journal of Animal Breeding and Genetics, 110(16), 385–392.
Zaorálek, L., Prilepok, M., & Snášel, V. (2015). Cattle identification using muzzle images. In Proceedings of the 2nd International Afro-European Conference for Industrial Advancement (AECIA) (pp. 105–115).
Noviyanto, A., & Arymurthy, A. M. (2012). Automatic cattle identification based on muzzle photo using speed-up robust features approach. In Proceedings of the 3rd European Conference of Computer Science (Vol. 110, p. 114).
Noviyanto, A., & Arymurthy, A. M. (2013). Beef cattle identification based on muzzle pattern using a matching refinement technique in the SIFT method. Computers and Electronics in Agriculture, 99, 77–84.
Lv, Z., Tek, A., Da Silva, F., Empereur-Mot, C., Chavent, M., & Baaden, M. (2013). Game on science-how video game technology may help biologists tackle visualization challenges. PLoS ONE, 8(3), e57990.
Awad, A. I. (2016). From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, 123, 423–435.
Wardrope, D. D. (1995). Problems [suppurating wounds] with the use of ear tags in cattle [Correspondence], Veterinary Record, 1995, (UK).
Kumar, S., Tiwari, S., & Singh, S.K. (2015). Face recognition for cattle. In 3rd International Conference on Image Information Processing (ICIIP) (pp. 65–72) Waknaghat, Shimla, India.
Kumar, S., Tiwari, S., & Singh, S. K. (2016). Face recognition of cattle: Can it be done? Proceedings of the National Academy of Sciences, India, Section A: Physical Sciences, 86(2), 137–148.
Kumar, S., Singh, S. K., Dutta, T., & Gupta, H. P. (2016). Poster: A real-time cattle recognition system using wireless multimedia networks. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications and Services Companion (pp. 48–48). Singapore.
Gaber, T., Tharwat, A., Hassanien, A. E., & Snasel, V. (2016). Biometric cattle identification approach based on Webers Local Descriptor and AdaBoost classifier. Computers and Electronics in Agriculture, 122, 55–66.
Awad, A. I., Zawbaa, H. M., Mahmoud, H. A., Nabi, E. H. H. A., Fayed, R. H., & Hassanien, A. E. (2013). A robust cattle identification scheme using muzzle print images. In Proceedings of IEEE International Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 529–534).
Andrew, W., Hannuna, S., Campbell, N., & Burghardt, T. (2016). Automatic individual holsteinfriesian cattle identification via selective local coat pattern matching in RGB-D imagery. In Proceedings on IEEE International Conference on Image Processing (ICIP) (pp. 484–488). Phoenix, AZ, USA.
Barron, U. G., Butler, F., McDonnell, K., & Ward, S. (2009). The end of the identity crisis? Advances in biometric markers for animal identification. Irish Veterinary Journal, 62(3), 204–208.
Johnston, A. M., & Edwards, D. S. (1996). Welfare implications of identification of cattle by ear tags. The Veterinary Record, 138(25), 612–614.
Feng, L., & Lv, Z. (2016). Plane surface detection and reconstruction using segment-based tensor voting. Journal of Visual Communication and Image Representation, 40(2), 831–837.
Su, T., Cao, Z., Lv, Z., Liu, C., & Li, X. (2016). Multi-dimensional visualization of large-scale marine hydrological environmental data. Advances in Engineering Software, 95, 7–15.
Su, T., Wang, W., Lv, Z., Wu, W., & Li, X. (2016). Rapid Delaunay triangulation for randomly distributed point cloud data using adaptive Hilbert curve. Computers and Graphics, 54, 65–74.
Mishra, S., Tomer, O. S., & Kalm, E. (1995). Muzzle dermatoglyphics: A new method to identify bovines. Asian Livestock, 91–96.
Cao, B., Kang, Y., Lin, S., Luo, X., Xu, S., Lv, Z., et al. (2016). A novel 3D model retrieval system based on three-view sketches. Journal of Intelligent and Fuzzy Systems, 31(5), 2675–2683.
Cao, B., Kang, Y., Lin, S., Luo, X., Xu, S., & Lv, Z. (2016). Style-sensitive 3D model retrieval through sketch-based queries. Journal of Intelligent & Fuzzy Systems, 31(5), 2637–2644.
Barry, B., Gonzales-Barron, U. A., McDonnell, K., Butler, F., & Ward, S. (2007). Using muzzle pattern recognition as a biometric approach for cattle identification. Transactions of the ASABE, 50(3), 1073–1080.
Minagawa, H., Fujimura, T., Ichiyanagi, M., Tanaka, K., & Fangquan, M. (2002). Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. In Proceedings of 3rd IEEE International Conference on Asian Agricultural Information Technology and Management AFITA 2002 (pp. 596–600).
Hyeon, K. T., Ikeda, Y., & Choi, H. L. (2005). The identification of Japanese black cattle by their faces. Asian-Australasian Journal of Animal Sciences, 18(6), 868–872.
Wu, W., Li, H., Su, T., Liu, H., & Lv, Z. (2016). GPU-accelerated SPH fluids surface reconstruction using two-level spatial uniform grids. The Visual Computer, 1–14.
Awad, A. I., Hassanien, A. E., & Zawbaa, H. M. (2013). A cattle identification approach using live captured muzzle print images. In Proceedings of Ist International Conference on Security of Information and Communication Networks (SecNet 2013) (143–152).
Lv, Z., Li, X., Zhang, B., Wang, W., Zhu, Y., Hu, J., & Feng, S. (2016). Managing big city information based on WebVRGIS. IEEE Access, 407–415.
Kumar, S., Singh, S. K., Datta, T., & Gupta, H. P. (2016). A fast cattle recognition system using smart devices. In Proceedings of the 2016 ACM Conference on Multimedia (pp. 742–743). Amsterdam, The Netherlands.
Cai, C., & Li, J. (2013). Cattle face recognition using local binary pattern descriptor. In Proceedings of IEEE International Conference on Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 1–4). Asia-Pacific, Taiwan.
Burghardt, T. (2008). Visual animal biometrics (Doctoral dissertation, Ph.D. thesis). UK: University of Bristol.
Corkery, G. P., Gonzales-Barron, U. A., Butler, F., Mc Donnell, K., & Ward S. (2007). A preliminary investigation on face recognition as a biometric identifier of sheep. Transactions of the ASABE, 50(1), 313–320.
Pisano, E. D., Zong, S., Hemminger, B. M., DeLuca, M., Johnston, R. E., Muller, K., et al. (1998). Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging, 11(4), 193–200.
Kumar, S., & Singh, S. K. (2014). Biometric recognition for pet animal. Journal of Software Engineering and Applications, 7(5), 470–482.
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277–1294.
Kumar, S., & Singh, S. K. (2016). Hybrid BFO and PSO swarm intelligence approach for biometric feature optimization. International Journal of Swarm Intelligence Research (IJSIR), 7(2), 36–62.
Kshirsagar, V. P., Baviskar, M. R., & Gaikwad, M. E. (2011, March). Face recognition using Eigenfaces. In Computer Research and Development (ICCRD), 2011 3rd International Conference on (Vol. 2, pp. 302–306). IEEE.
Kumar, S., & Singh, S. K. (2015). Feature selection and recognition of face by using hybrid chaotic PSO-BFO and appearance-based recognition algorithms. International Journal of Natural Computing Research (IJNCR), 5(3), 26–53.
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720.
Etemad, K., & Chellappa, R. (1997). Discriminant analysis for recognition of human face images. JOSA A, 14(8), 1724–1733.
Kumar, S., Datta, D., & Singh, S. K. (2015). Black hole algorithm and its applications. In Computational intelligence applications in modeling and control (pp. 147–170).
Liu, C., & Wechsler, H. (1999). Comparative assessment of independent component analysis (ICA) for face recognition. In International Conference on Audio and Video Based Biometric Person Authentication (pp. 22–24).
Weng, J., Zhang, Y., & Hwang, W. S. (2003). Candid covariance-free incremental principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), 1034–1040.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. (2013). Face recognition by independent component analysis. IEEE Transactions on Neural Networks, 13(6), 1450–1464.
Kim, T. K., Wong, S. F., Stenger, B., Kittler, J., & Cipolla, R. (2007). Incremental linear discriminant analysis using sufficient spanning set approximations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–8).
Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.
Burt, P., & Adelson, E. (1983). The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4), 532–540.
Ross, A. A., Nandakumar, K., & Jain, A. (2006). Handbook of multibiometrics (Vol. 6). Springer Science and Business Media.
Kumar, S., Singh, S. K., Singh, R. S., Singh, A. K., & Tiwari, S. (2016). Real-time recognition of cattle using animal biometrics. Journal of Real-Time Image Processing, 1–22. https://doi.org/10.1007/s11554-016-0645-4.
Kumar, S., & Singh, S. (2016). Visual animal biometrics: Survey. IET Biometrics, 1–38. https://doi.org/10.1049/iet-bmt.2016.0017.
Andrew, W., Hannuna, S., Campbell, N., & Burghardt, T. (2016). Automatic individual holsteinfriesian cattle identification via selective local coat pattern matching in RGB-D imagery. In Proceedings of IEEE International Conference on Image Processing (ICIP) (pp. 484–488).
Kumar, S., & Singh, S. K. (2016). Monitoring of pet animal in smart cities using animal biometrics. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2016.12.006.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kumar, S., Singh, S.K., Singh, R., Singh, A.K. (2017). Muzzle Point Pattern-Based Techniques for Individual Cattle Identification. In: Animal Biometrics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7956-6_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-7956-6_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7955-9
Online ISBN: 978-981-10-7956-6
eBook Packages: Computer ScienceComputer Science (R0)