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Recognition of Cattle Using Face Images

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Abstract

In this chapter, a cattle recognition system is proposed. The proposed cattle recognition system uses the face image for identification of cattle using computer vision approaches. The major research contributions of this research are in three folds: (1) the preparations of a facial image database of cattle, (2) extraction of discriminatory set of features from the cattle’s face image database and implementation of computer vision-based face recognition representation algorithms for recognizing individual cattle, and (3) finally, the experimental results and discussion of face recognition algorithms. Thus, this chapter presents a comprehensive review of the performances of various computer vision and pattern recognition approaches for the application of cattle face recognition.

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References

  1. Wagyu Registry Association. (2009). The handbook for Wagyu registration. Kyoto: Wagyu Registry Association.

    Google Scholar 

  2. Robb, J. G., & Rosa, E. L. (2004). Some issues related to beef traceability: Transforming cattle into beef in the United States. In US livestock identification systems: Risk management and market opportunities (p. 7). Tucson, AZ: Western Extension Marketing Committee.

    Google Scholar 

  3. Graziano, J. F. (1982). da Silva. A Modernização Dolorosa. Zahar: Rio de Janeiro.

    Google Scholar 

  4. Kühl, H. S., & Burghardt, T. (2013). Animal biometrics: quantifying and detecting phenotypic appearance. Trends in ecology & evolution, 28(7), 432–441.

    Google Scholar 

  5. Burghardt, T. (2008). Visual animal biometrics: Automatic detection and individual identification by coat pattern. Doctoral dissertation. University of Bristol.

    Google Scholar 

  6. Fraser, C., Riley, S., Anderson, R. M., & Ferguson, N. M. (2004). Factors that make an infectious disease outbreak controllable. In Proceedings of the National Academy of Sciences of the United States of America (Vol. 101, No. 16, pp. 6146–6151).

    Google Scholar 

  7. Fumière, O., Veys, P., Boix, A., Baeten, V., & Berben, G. (2009). Methods of detection, species identification and quantification of processed animal proteins in feedingstuffs. Biotechnologie, Agronomie, Société et Environnement, 13(s), 59–70.

    Google Scholar 

  8. Wilkinson, I. S., Chilvers, B. L., Duignan, P. J., & Pistorius, P. A. (2011). An evaluation of hot-iron branding as a permanent marking method for adult New Zealand sea lions. Phocarctoshookeri. Wildlife Research, 38(1), 51–60.

    Article  Google Scholar 

  9. Bowling, M. B., Pendell, D. L., Morris, D. L., Yoon, Y., Katoh, K., Belk, K. E., et al. (2008). Identification and traceability of cattle in selected countries outside of North America. The Professional Animal Scientist, 24(4), 287–294.

    Article  Google Scholar 

  10. Johnston, A. M., Edwards, D. S., Hofmann, E., Wrench, P. M., Sharples, F. P., Hiller, R. G., et al. (1996). 1418001. Welfare implications of identification of cattle by ear tags. The Veterinary Record, 138(25), 612–614.

    Article  Google Scholar 

  11. Bolle, R. M., Connell, J. H., Pankanti, S., Ratha, N. K., & Senior, A. W. (2005, October). The relation between the ROC curve and the CMC. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies, 2005 (pp. 15–20). New York: IEEE.

    Google Scholar 

  12. http://cattle-today.com/. Retrieved 20 March 2014.

  13. Marchant, J., (2002). Secure animal identification and source verification. JM Communications, UK. Copyright Optibrand Ltd., LLC.

    Google Scholar 

  14. Caja, G., Conill, C., Nehring, R., & Ribó, O. (1999). Development of a ceramic bolus for the permanent electronic identification of sheep, goat and cattle. Computers and Electronics in Agriculture, 24(1), 45–63.

    Article  Google Scholar 

  15. Beadles, M. L., Miller, J. A., Shelley, B. K., & Ingenhuett, D. P. (1979). Comparison of the efficacy of ear tags, leg bands, and tail tags for control of the horn fly on range cattle. Southwestern Entomologist.

    Google Scholar 

  16. Hayes, N. J., Shaw, R. J., Hayes Norman, J., & Shaw Richard, J. (1986). Multiple purpose animal ear tag system. U.S. Patent 4,612,877.

    Google Scholar 

  17. Ritchey, E. B., Ritchey Manufacturing, Inc. (2008). Tag for livestock. U.S. Patent 7,441,354.

    Google Scholar 

  18. Ng, M. L., Leong, K. S., Hall, D. M., & Cole, P. H. (2005, August). A small passive UHF RFID tag for livestock identification. In IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2005. MAPE 2005 (Vol. 1, pp. 67–70). New York: IEEE.

    Google Scholar 

  19. Walsh, B. J. H., Intermec Ip Corp. (1999). Radio frequency tag. U.S. Patent 5,995,006.

    Google Scholar 

  20. Chao, C. C., Yang, J. M., & Jen, W. Y. (2007). Determining technology trends and forecasts of RFID by a historical review and bibliometric analysis from 1991 to 2005. Technovation, 27(5), 268–279.

    Article  Google Scholar 

  21. Glasser, D. J., Goodman, K. W., & Einspruch, N. G. (2007). Chips, tags and scanners: Ethical challenges for radio frequency identification. Ethics and Information Technology, 9(2), 101–109.

    Article  Google Scholar 

  22. Roberts, C. M. (2006). Radio frequency identification (RFID). Computers & Security, 25(1), 18–26.

    Article  Google Scholar 

  23. McInerney, J. P., Howe, K. S., & Schepers, J. A. (1992). A framework for the economic analysis of disease in farm livestock. Preventive Veterinary Medicine, 13(2), 137–154.

    Article  Google Scholar 

  24. Hall, A., Sulaiman, R., & Bezkorowajnyj, P. G. (2007). Reframing technical change: Livestock fodder scarcity revisited as innovation capacity scarcity—A conceptual Framework. ILRI and UNU/MERIT.

    Google Scholar 

  25. Havlikova, M., Kroeze, C., & Huijbregts, M. A. J. (2008). Environmental and health impact by dairy cattle livestock and manure management in the Czech Republic. Science of the Total Environment, 396(2), 121–131.

    Article  Google Scholar 

  26. Rusk, C. P., Blomeke, C. R., Balschweid, M. A., Elliot, S. J., & Baker, D. (2006). An evaluation of retinal imaging technology for 4-H beef and sheep identification. Journal of Extension, 44(5), 1–33.

    Google Scholar 

  27. Vlad, M., Parvulet, R. A., & Vlad, M. S. (2012). A survey of livestock identification systems. In Proceedings of 13th WSEAS International Conference on Automation and Information(ICAI12) (pp 165–170).

    Google Scholar 

  28. Barron, U. G., Corkery, G., Barry, B., Butler, F., McDonnell, K., & Ward, S. (2008). Assessment of retinal recognition technology as a biometric method for sheep identification. Journal of Computational Electronics in Agriculture, 60(2), 156–166.

    Article  Google Scholar 

  29. Bharadwaj, S., Bhatt, H. S., Vatsa, M., & Singh, R. (2016). Domain specific learning for newborn face recognition. IEEE Transactions on Information Forensics and Security, 11(7), 1630–1641.

    Google Scholar 

  30. Adelson, E. H., Anderson, C. H., Bergen, J. R., Burt, P. J., & Ogden, J. M. (1984). Pyramid methods in image processing. RCA Engineer, 29(6), 33–41.

    Google Scholar 

  31. Burt, P., & Adelson, E. (1983). The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4), 532–540.

    Article  Google Scholar 

  32. Reza, A. M. (2004). Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. The Journal of VLSI Signal Processing, 38(1), 35–44.

    Article  Google Scholar 

  33. Zuiderveld, K. (1994, August). Contrast limited adaptive histogram equalization. In Graphics Gems IV (pp. 474–485). Academic Press Professional, Inc.

    Google Scholar 

  34. Turk, M. A., & Pentland, A. P. (1991, June). Face recognition using Eigenfaces. In Proceedings CVPR’91, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991 (pp. 586–591). New York: IEEE.

    Google Scholar 

  35. Aishwarya, P., & Marcus, K. (2010). Face recognition using multiple Eigenface subspaces. Journal of Engineering and Technology Research, 2(8), 139–143.

    Google Scholar 

  36. Yang, M. H. (2002, May). Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using Kernel methods. In Fgr (Vol. 2, p. 215).

    Google Scholar 

  37. Yu, H., & Yang, J. (2001). A direct LDA algorithm for high-dimensional data—With application to face recognition. Pattern Recognition, 34(10), 2067–2070.

    Article  MATH  Google Scholar 

  38. Zhao, W., Chellappa, R., & Krishnaswamy, A. (1998, April). Discriminant analysis of principal components for face recognition. In Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998 (pp. 336–341). New York: IEEE.

    Google Scholar 

  39. Wang, T. (2017, July). A novel face recognition method based on ICA and binary tree SVM. In Proceeding of IEEE International Conference on Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC) (Vol. 1, pp. 251–254). New York: IEEE.

    Google Scholar 

  40. Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.

    Article  MATH  Google Scholar 

  41. 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.

    Article  Google Scholar 

  42. Li, M., & Yuan, B. (2005). 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, 26(5), 527–532.

    Article  Google Scholar 

  43. Kim, T. K., Stenger, B., Kittler, J., & Cipolla, R. (2011). Incremental linear discriminant analysis using sufficient spanning sets and its applications. International Journal of Computer Vision, 91(2), 216–232.

    Article  MathSciNet  MATH  Google Scholar 

  44. Zhao, H., & Yuen, P. C. (2008). Incremental linear discriminant analysis for face recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(1), 210–221.

    Google Scholar 

  45. 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.

    Google Scholar 

  46. Domeniconi, C., & Gunopulos, D. (2001). Incremental support vector machine construction. In Proceedings IEEE International Conference on Data Mining, 2001. ICDM 2001 (pp. 589–592). New York: IEEE.

    Google Scholar 

  47. Fung, G., & Mangasarian, O. L. (2002, April). Incremental support vector machine classification. In Proceedings of the 2002 SIAM International Conference on Data Mining (pp. 247–260). Society for Industrial and Applied Mathematics.

    Google Scholar 

  48. Laskov, P., Gehl, C., Krüger, S., & Müller, K. R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.

    Google Scholar 

  49. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001(Vol. 1, pp. I–I). New York: IEEE.

    Google Scholar 

  50. Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys (CSUR), 35(4), 399–458.

    Article  Google Scholar 

  51. Burghardt, T., & Campbell, N. (2007). Individual animal identification using visual biometrics on deformable coat patterns. In International Conference on Computer Vision Systems (ICVS07) (Vol. 5, pp. 1–10).

    Google Scholar 

  52. Eradus, W. J., & Jansen, M. B. (1999). Animal identification and monitoring. Computers and Electronics in Agriculture, 24(1), 91–98.

    Article  Google Scholar 

  53. Kamencay, P., Trnovszky, T., Benco, M., Hudec, R., Sykora, P., & Satnik, A. (2016, May). Accurate wild animal recognition using PCA, LDA and LBPH. In Proceedings of IEEE International Conference on ELEKTRO (pp. 62–67).

    Google Scholar 

  54. Estrada, A., Garber, P. A., Rylands, A. B., Roos, C., Fernandez-Duque, E., Di Fiore, A., & Nekaris K. A. I. et al. (2017). “Impending extinction crisis of the world’s primates: Why primates matter.” Science Advances, 31, e1600946.

    Google Scholar 

  55. Cowlishaw, G., & Dunbar, R. I. M. (2000). Primate conservation biology. University of Chicago Press.

    Google Scholar 

  56. Parr, L. A., Dove, T., Hopkins, W. D. (1998). Why faces may be special: Evidence of the inversion effect in chimpanzees. Journal of Cognitive Neuroscience, 10(5), 615–622.

    Google Scholar 

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Correspondence to Santosh Kumar .

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Kumar, S., Singh, S.K., Singh, R., Singh, A.K. (2017). Recognition of Cattle Using Face Images. In: Animal Biometrics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7956-6_3

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  • DOI: https://doi.org/10.1007/978-981-10-7956-6_3

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