Skip to main content

Analytical Study of Animal Biometrics: A Technical Survey

Abstract

This chapter is dedicated to the comprehensive survey on the current state-of-the-art in the field of animal biometrics. In addition to this, we have provided a brief introduction to the discipline of animal biometrics followed by the classification and identification techniques of species or individual animal using the discriminatory set of their biometric features in brief. Further, the potential challenges of existing methods and research communities, tools, and data sharing are also discussed.

Keywords

  • Identification
  • Morphological image pattern
  • Coat pattern
  • Detection
  • Feature extraction
  • Feature representation
  • Classification
  • Computer vision
  • Learning model
  • Pattern recognition

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-10-7956-6_2
  • Chapter length: 58 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-981-10-7956-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.00
Price excludes VAT (USA)
Hardcover Book
USD   109.00
Price excludes VAT (USA)
Fig. 2.1
Fig. 2.2
Fig. 2.3

Source Dairying and Husbandry, Institute of Agriculture Sciences, (BHU, Varanasi)

Fig. 2.4
Fig. 2.5
Fig. 2.6
Fig. 2.7
Fig. 2.8
Fig. 2.9
Fig. 2.10
Fig. 2.11
Fig. 2.12
Fig. 2.13
Fig. 2.14
Fig. 2.15
Fig. 2.16
Fig. 2.17

References

  1. Lettink, M., & Hare, K. M. (2016). Sampling techniques for New Zealand lizards. In New Zealand lizards (pp. 269–291). Springer International Publishing.

    Google Scholar 

  2. Gregory, A. L., Berger-Tal, O., Blumstein, D. T., Angeloni, L., Bessa-Gomes, C., Blackwell, B. F., et al. (2016). Research priorities from animal behaviour for maximising conservation progress. Trends in Ecology & Evolution, 31(12), 953–964.

    CrossRef  Google Scholar 

  3. Porto, S. M., Arcidiacono, C., Anguzza, U., & Cascone, G. (2013). A computer vision-based system for the automatic detection of lying behaviour of dairy cows in free-stall barns. Biosystems Engineering, 115(2), 184–194.

    CrossRef  Google Scholar 

  4. Caja, G., Díaz-Medina, E., Salama, A. A. K., Salama, O. A. E., El-Shafie, M. H., El-Metwaly, H. A., et al. (2016). Comparison of visual and electronic devices for individual identification of dromedary camels under different farming conditions. Journal of Animal Science, 94(8), 3561–3571.

    CrossRef  Google Scholar 

  5. Wamba, S. F., & Wicks, A. (2010, June). RFID deployment and use in the dairy value chain: Applications, current issues and future research directions. In Proceedings of IEEE International Symposium on Technology and Society (ISTAS) (pp. 172–179).

    Google Scholar 

  6. Wallace, L. E., Paterson, J. A., Clark, R., Harbac, M., & Kellom, A. (2008). Readability of thirteen different radio frequency identification ear tags by three different multi-panel reader systems for use in beef cattle. The Professional Animal Scientist, 24(5), 384–391.

    CrossRef  Google Scholar 

  7. Ruiz-Garcia, L., & Lunadei, L. (2011). The role of RFID in agriculture: Applications, limitations and challenges. Computers and Electronics in Agriculture, 79(1), 42–50.

    CrossRef  Google Scholar 

  8. Trevarthen, A. (2007). The national livestock identification system: The importance of traceability in e-business. Journal of Theoretical and Applied Electronic Commerce Research, 2(1).

    Google Scholar 

  9. Turner, L. W., Udal, M. C., Larson, B. T., & Shearer, S. A. (2000). Monitoring cattle behavior and pasture use with GPS and GIS. Canadian Journal of Animal Science, 80(3), 405–413.

    CrossRef  Google Scholar 

  10. 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.

    CrossRef  Google Scholar 

  11. Feng, J., Fu, Z., Wang, Z., Xu, M., & Zhang, X. (2013). Development and evaluation on a RFID-based traceability system for cattle/beef quality safety in China. Food Control, 31(2), 314–325.

    CrossRef  Google Scholar 

  12. Gaber, T., Tharwat, A., Hassanien, A. E., & Snasel, V. (2016). Biometric cattle identification approach based on weber’s local descriptor and AdaBoost classifier. Computers and Electronics in Agriculture, 122, 55–66.

    CrossRef  Google Scholar 

  13. Huhtala, A., Suhonen, K., Mäkelä, P., Hakojärvi, M., & Ahokas, J. (2007). Evaluation of instrumentation for cow positioning and tracking indoors. Biosystems Engineering, 96(3), 399–405.

    CrossRef  Google Scholar 

  14. 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.

    CrossRef  Google Scholar 

  15. Neary, M., & Yager, A. (2002). Methods of livestock identification.

    Google Scholar 

  16. Leslie, E., Hernández-Jover, M., Newman, R., & Holyoake, P. (2010). Assessment of acute pain experienced by piglets from ear tagging, ear notching and intraperitoneal injectable transponders. Applied Animal Behaviour Science, 127(3), 86–95.

    CrossRef  Google Scholar 

  17. Pennington, J. A. (2007). Tattooing of Cattle and Goats. Cooperative Extension Service, University of Arkansas Division of Agriculture, US Department of Agriculture, and county governments cooperating.

    Google Scholar 

  18. Stanford, K., Stitt, J., Kellar, J. A., & McAllister, T. A. (2001). Traceability in cattle and small ruminants in Canada. Revue Scientifique et Technique-Office International des Epizooties, 20(2), 510–522.

    CrossRef  Google Scholar 

  19. Knosby, A. T., & Knosby Austin, T. (1986). Livestock identification system. U.S. Patent 4,597,495.

    Google Scholar 

  20. Noonan, G. J., Rand, J. S., Priest, J., Ainscow, J., & Blackshaw, J. K. (1994). Behavioural observations of piglets undergoing tail docking, teeth clipping and ear notching. Applied Animal Behaviour Science, 39(3–4), 203–213.

    CrossRef  Google Scholar 

  21. Hilpert, J. J., & Allflex New Zealand Limited. (2003). Animal ear tag. U.S. Patent 6,666,170.

    Google Scholar 

  22. Wardrope, D. D. (1995). Problems with the use of ear tags in cattle. Veterinary Record, 137(26), 675.

    Google Scholar 

  23. 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.

    CrossRef  Google Scholar 

  24. Geissler, Randolph K., Steven Lewis, and Scott Alan Nelson (2011). Radio frequency animal tracking system. U.S. Patent 7,965,188.

    Google Scholar 

  25. Voulodimos, A. S., Patrikakis, C. Z., Sideridis, A. B., Ntafis, V. A., & Xylouri, E. M. (2010). A complete farm management system based on animal identification using RFID technology. Computers and Electronics in Agriculture, 70(2), 380–388.

    CrossRef  Google Scholar 

  26. Byrd, G. (2015). Tracking cows wirelessly. Computer, 48(6), 60–63.

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  28. Rojas-Olivares, M. A., Caja, G., Carné, S., Salama, A. A. K., Adell, N., & Puig, P. (2012). Determining the optimal age for recording the retinal vascular pattern image of lambs. Journal of Animal Science, 90(3), 1040–1046.

    CrossRef  Google Scholar 

  29. Klindtworth, M., Wendl, G., Klindtworth, K., & Pirkelmann, H. (1999). Electronic identification of cattle with injectable transponders. Computers and Electronics in Agriculture, 24(1), 65–79.

    CrossRef  Google Scholar 

  30. Ismail, R., & Ismail, I. (2013, August). Development of graphical user interface (GUI) for livestock management system. In Proceedings 4th IEEE International Conference on Control and System Graduate Research Colloquium (ICSGRC) (pp. 43–47).

    Google Scholar 

  31. Allen, A., Golden, B., Taylor, M., Patterson, D., Henriksen, D., & Skuce, R. (2008). Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland. Livestock Science, 116(1), 42–52.

    CrossRef  Google Scholar 

  32. 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.

    Google Scholar 

  33. Ahmed, S., Gaber, T., Tharwat, A., Hassanien, A. E., & Snáel, V. (2015, September). Muzzle-based cattle identification using speed up robust feature approach. In Proceedings on IEEE International Conference on Intelligent Networking and Collaborative Systems (INCOS) (pp. 99–104).

    Google Scholar 

  34. Hiby, L., Lovell, P., Patil, N., Kumar, N. S., Gopalaswamy, A. M., & Karanth, K. U. (2009). A tiger cannot change its stripes: Using a three-dimensional model to match images of living tigers and tiger skins. Biology Letters (rsbl-2009).

    Google Scholar 

  35. 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.

    CrossRef  Google Scholar 

  36. Awad, A. I., Zawbaa, H. M., Mahmoud, H. A., Nabi, E. H. H. A., Fayed, R. H., & Hassanien, A. E. (2013, September). A robust cattle identification scheme using muzzle print images. In Proceedings of IEEE Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 529–534).

    Google Scholar 

  37. Mahmoud, H. A., & Hadad, H. M. R. E. (2015). Automatic cattle muzzle print classification system using multiclass support vector machine. International Journal of Image Mining, 1(1), 126–140.

    CrossRef  Google Scholar 

  38. Baranov, A.S., Graml, R., Pirchner, F., Schmid, D.O., 1993. Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle. Journal of Animal Breeding and Genetics 110 (1–6), 385–392.

    Google Scholar 

  39. 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 

  40. Rojas-Olivares, M. A., Caja, G., Carné, S., Salama, A. A. K., Adell, N., & Puig, P. (2011). Retinal image recognition for verifying the identity of fattening and replacement lambs. Journal of Animal Science, 89(8), 2603–2613.

    CrossRef  Google Scholar 

  41. Davis, K. M., Smith, T., Bolt, B., Meadows, S., Powell, J. G., Vann, R. C., et al. (2015). Digital quantification of eye pigmentation of cattle with white faces. Journal of Animal Science, 93(7), 3654–3660.

    CrossRef  Google Scholar 

  42. Adell, N., Puig, P., Rojas-Olivares, A., Caja, G., Carné, S., & Salama, A. A. (2012). A bivariate model for retinal image identification in lambs. Computers and Electronics in Agriculture, 87, 108–112.

    CrossRef  Google Scholar 

  43. 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. Computers and Electronics in Agriculture, 60(2), 156–166.

    CrossRef  Google Scholar 

  44. 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.

    CrossRef  Google Scholar 

  45. Kumar, S., & Singh, S. K. (2014). Biometric recognition for pet animal. Journal of Software Engineering and Applications, 7(05), 470.

    CrossRef  Google Scholar 

  46. Shanahan, C., Kernan, B., Ayalew, G., McDonnell, K., Butler, F., & Ward, S. (2009). A framework for beef traceability from farm to slaughter using global standards: An Irish perspective. Computers and Electronics in Agriculture, 66(1), 62–69.

    CrossRef  Google Scholar 

  47. Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148–1161.

    CrossRef  Google Scholar 

  48. Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, 93(26), 429–441.

    Google Scholar 

  49. Chen, W. K., Lee, J. C., Han, W. Y., Shih, C. K., & Chang, K. C. (2013). Iris recognition based on bidimensional empirical mode decomposition and fractal dimension. Information Sciences, 221, 439–451.

    CrossRef  Google Scholar 

  50. Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5), 961–1005.

    MathSciNet  MATH  CrossRef  Google Scholar 

  51. Lu, Y., He, X., Wen, Y., & Wang, P. S. (2014). A new cow identification system based on iris analysis and recognition. International Journal of Biometrics, 6(1), 18–32.

    CrossRef  Google Scholar 

  52. Kumar, S., Pandey, A., Satwik, K.S.R., Kumar, S., Singh, S.K., Singh, A.K. and Mohan, A., 2018. Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement, 116, pp.1-17.

    Google Scholar 

  53. Kumar, S., Singh, S.K., Abidi, A.I., Datta, D. and Sangaiah, A.K., 2017. Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images. International Journal of Parallel Programming, pp.1-26.

    Google Scholar 

  54. Schroeder, T. C., & Tonsor, G. T. (2012). International cattle ID and traceability: Competitive implications for the US. Food Policy, 37(1), 31–40.

    CrossRef  Google Scholar 

  55. Murphy, G. L., Scanga, J. A., Belk, K. E., Smith, G. C., Pendell, D. L., & Morris, D. L. (2008). Animal identification systems in North America. The Professional Animal Scientist, 24(4), 277–286.

    CrossRef  Google Scholar 

  56. Mishra, S., 1994. Studies on the characteristics of muzzle dermatoglyphics in dairy cattle and buffalo (Doctoral dissertation, NDRI, Karnal).

    Google Scholar 

  57. Johansson, I., & Venge, O. (1951). Studies on the value of various morphological characters for the diagnosis of monozygocity of cattle twins. Journal of Animal Breeding and Genetics, 59(4), 389-424.

    Google Scholar 

  58. Kumar, S., & Singh, S. K. (2016). Feature selection and recognition of muzzle point image pattern of cattle by using hybrid chaos BFO and PSO algorithms. In Advances in Chaos Theory and Intelligent Control (pp. 719–751).

    Google Scholar 

  59. Bowyer, K. W., Hollingsworth, K., & Flynn, P. J. (2008). Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding, 110(2), 281–307.

    CrossRef  Google Scholar 

  60. Cai, C., & Li, J. (2013, October). 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).

    Google Scholar 

  61. Kumar, S., & Singh, S. K. (2016). Automatic identification of cattle using muzzle point pattern: A hybrid feature extraction and classification paradigm. Multimedia Tools and Applications, 1–30.

    Google Scholar 

  62. Kim, H. 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.

    CrossRef  Google Scholar 

  63. El-Henawy, I., El Hadad, H. M., & Mastorakis, N. (2016). Muzzle Feature Extraction Based on gray level co-occurrence matrix. International Journal of Veterinary Medicine, 1, 16–24.

    Google Scholar 

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

    Google Scholar 

  65. Jain, A., Bolle, R., & Pankanti, S. (Eds.). (2006). Biometrics: Personal identification in networked society (Vol. 479). Berlin: Springer Science & Business Media.

    Google Scholar 

  66. Petersen, W. E. (1922). The identification of the bovine by means of nose-prints1. Journal of Dairy Science, 5(3), 249–258.

    CrossRef  Google Scholar 

  67. Minagawa, H., Fujimura, T., Ichiyanagi, M., & Tanaka, K. (2002). Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. Publications of the Japanese Society of Agricultural Informatics, 8, 596–600.

    Google Scholar 

  68. Zaorálek, L., Prilepok, M., & Snášel, V. (2016). Cattle identification using muzzle images. In Proceedings of 2nd International Afro-European Conference for Industrial Advancement AECIA (pp. 105–115). Cham: Springer.

    Google Scholar 

  69. 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 

  70. El-Bakry, H. M., El-Hennawy, I., & El Hadad, H. M. (2014). Bovines muzzle identification using box-counting. International Journal of Computer Science and Information Security, 12(5), 29.

    Google Scholar 

  71. Leick, W. D. S., Tecnologia computacional de apoio a rastreabilidade biométrica de bovinos. Doctoral dissertation. Universidade de São Paulo.

    Google Scholar 

  72. Hosseini, H. (2015). Animal muzzle pattern scanning device. U.S. Patent Application 14/969,511.

    Google Scholar 

  73. Tharwat, A., Gaber, T., Hassanien, A. E., Hassanien, H. A., & Tolba, M. F. (2014). Cattle identification using muzzle print images based on texture features approach. In Proceedings of the 5th International Conference on Innovations in Bio-Inspired Computing and Applications IBICA (pp. 217–227).

    Google Scholar 

  74. Ahmed, S., Gaber, T., Tharwat, A., Hassanien, A. E., & Snáel, V. (2015, September). Muzzle-based cattle identification using speed up robust feature approach. In Proceedings of IEEE International Conference on Intelligent Networking and Collaborative Systems (INCOS) (pp. 99–104).

    Google Scholar 

  75. El-Henawy, I., El Bakry, H. M., & El Hadad, H. M. (2016). Cattle identification using segmentation-based fractal texture analysis and artificial neural networks. International Journal of Electronics and Information Engineering, 4(2), 82–93.

    Google Scholar 

  76. Edwin, A., & George, M., Fuzzy Mathematical Approach for Cattle Identification.

    Google Scholar 

  77. Carbayo, F., & Marques, A. C. (2011). The costs of describing the entire animal kingdom. Trends in Ecology & Evolution, 26(4), 154–155.

    CrossRef  Google Scholar 

  78. http://ecovision.mit.edu/~sloop/joshresults/.

  79. http://ecovision.mit.edu/~sloop/joshresults/inpainted3pixdisk.html.

  80. Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37.

    CrossRef  Google Scholar 

  81. Kumar, S., & Singh, S. K. (2016). Monitoring of pet animal in smart cities using animal biometrics. Future Generation Computer Systems.

    Google Scholar 

  82. Burghardt, T., & Calic, J. (2006, September). Real-time face detection and tracking of animals. In Proceedings of 8th IEEE Seminar on Neural Network Applications in Electrical Engineering (pp. 27–32).

    Google Scholar 

  83. Ronque, M.U., Azevedo-Silva, M., Mori, G.M., Souza, A.P. and Oliveira, P.S., 2016. Three ways to distinguish species: using behavioral, ecological, and molecular data to tell apart two closely related ants, Camponotus renggeri and Camponotus rufipes (Hymenoptera: Formicidae). Zoological Journal of the Linnean Society, 176(1), pp.170-181.

    Google Scholar 

  84. Jhuang, H. et al. (2010) Automated home-cage behavioural phenotyping of mice. Nat. Commun. 1, Article 68.

    Google Scholar 

  85. Kastberger, G., Maurer, M., Weihmann, F., Ruether, M., Hoetzl, T., Kranner, I., et al. (2011). Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees. Frontiers in Zoology, 8(1), 3.

    CrossRef  Google Scholar 

  86. Papadakis, V.M. (2012) A computer-vision system and methodology for the analysis of fish behaviour. Aquac. Eng. 46, 53–59.

    Google Scholar 

  87. Tweed, D., & Calway, A. (2002, October). Tracking many objects using subordinated CONDENSATION. BMVC, 1–10.

    Google Scholar 

  88. Hannuna, S. L., Campbell, N. W., & Gibson, D. P. (2005, September). Identifying quadruped gait in wildlife video. In Proceedings of IEEE International conference on Image Processing (ICIP) (Vol. 1, pp. I–713).

    Google Scholar 

  89. Ramanan, D., Forsyth, D. A., & Barnard, K. (2006). Building models of animals from video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8), 1319–1334.

    CrossRef  Google Scholar 

  90. Domeier, M. L., & Nasby-Lucas, N. (2007). Annual re-sightings of photographically identified white sharks (Carcharodon carcharias) at an eastern Pacific aggregation site (Guadalupe Island, Mexico). Marine Biology, 150(5), 977–984.

    CrossRef  Google Scholar 

  91. Collins, L. M. (2008). Non-intrusive tracking of commercial broiler chickens in situ at different stocking densities. Applied Animal Behaviour Science, 112(1), 94–105.

    CrossRef  Google Scholar 

  92. Collins, L. M., Asher, L., Pfeiffer, D. U., Browne, W. J., & Nicol, C. J. (2011). Clustering and synchrony in laying hens: The effect of environmental resources on social dynamics. Applied Animal Behaviour Science, 129(1), 43–53.

    CrossRef  Google Scholar 

  93. Ernst, A., & Ku blbeck, C. (2011). Fast face detection and species classification of African great apes. In Proceedings of 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Klagenfurt (pp. 279–284).

    Google Scholar 

  94. Ríos, M., Danowitz, M., & Solounias, N. (2016). First comprehensive morphological analysis on the metapodials of Giraffidae. Palaeontologia Electronica, 19(3), 1–39.

    Google Scholar 

  95. Zhang, W., Sun, J., & Tang, X. (2011). From tiger to panda: Animal head detection. IEEE Transactions on Image Processing, 20, 1696–1708.

    MathSciNet  MATH  CrossRef  Google Scholar 

  96. Zhang, W. (2008). Cat head detection: How to effectively exploit shape and texture features. Lecture Notes in Computer Science, 5305, 802–816.

    CrossRef  Google Scholar 

  97. Sagonas, C., Panagakis, Y., Zafeiriou, S., & Pantic, M. (2016). Robust statistical frontalization of human and animal faces. International Journal of Computer Vision, 1–22.

    Google Scholar 

  98. Chen, Y.-C., Hidayati, S. C., Cheng, W.-H., Hu, M.-C., & Hua, K.-L. (2016). Locality constrained sparse representation for cat recognition. In Proceedings of 22nd International Conference on MMM 2016, Miami, FL, USA (pp. 140–151).

    Google Scholar 

  99. Jarraya, I., Ouarda, W., & Alimi, A. M. (2015). A preliminary investigation on horses recognition using facial texture features. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kowloon (pp. 2803–2808).

    Google Scholar 

  100. Galimberti, F., & Sanvito, S. (2016). ‘Environmental research at Sea Lion Island’, Falkland Islands Field work report 2015/2016.

    Google Scholar 

  101. Chen, J., Wen, Q., Qu, W., & Mete M. (2012). Panda facial region detection based on topology modelling. In Proceedings of 5th International Congress on Image and Signal Processing (CISP) (pp. 911–915).

    Google Scholar 

  102. Qi, Y., Cinar,G. T., Souza, V. M. A., Batista, G. E. A. P. A., Wang, Y., & Principe J. C. (2015). Effective insect recognition using a stacked autoencoder with maximum correntropy criterion. In Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), Killarney (pp. 1–7).

    Google Scholar 

  103. Carlos, J., Reyesvera, U., & Possani-Espinosa, A. (2016). Scorpions: Classification of poisonous species using shape features. In Proceedings of International Conference on Electronics Communications and Computers (CONIELECOMP) (pp. 125–129).

    Google Scholar 

  104. Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., & Belongie, S. (2015). Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 595–604).

    Google Scholar 

  105. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. Proceedings of International Journal of Computer Vision, 60(2), 91–110.

    CrossRef  Google Scholar 

  106. Azhar, M. A. H. B., Hoque, S., & Deravi, F. (2012). Automatic identification of wildlife using local binary patterns. In Proceedings of International Conference in IET Conference on Image Processing (IPR 2012), London (pp. 1–6).

    Google Scholar 

  107. Kumar, S., & Singh, S. K. (2016). Visual animal biometrics: Survey. IET Biometrics, 6(3), 139–156.

    CrossRef  Google Scholar 

  108. Petrovska-Delacretaz, D., Edwards, A., Chiassoli, J., Chollet, G., & Pilliod, D. S. (2014, March). A reference system for animal biometrics: Application to the northern leopard frog. In Proceedings of 1st IEEE International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 295–300).

    Google Scholar 

  109. Bhusal, S., Goel, S., Khanal, K., Taylor, M., & Karkee, M. (2017). Bird detection, tracking and counting in wine grapes. In Proceedings of Annual International Meeting on American Society of Agricultural and Biological Engineers (ASABE) (p. 1).

    Google Scholar 

  110. Takeki, A., Trinh, T. T., Yoshihashi, R., Kawakami, R., Iida, M., & Naemura, T. (2016). Combining deep features for object detection at various scales: Finding small birds in landscape images. IPSJ Transactions on Computer Vision and Applications, 8(1), 5.

    CrossRef  Google Scholar 

  111. Li, W., & Song, D. (2015). Automatic bird species filtering using a multimodel approach. IEEE Transactions on Automation Science and Engineering, 12(2), 553–564.

    CrossRef  Google Scholar 

  112. Atanbori, J., Duan, W., Murray, J., Appiah, K., & Dickinson, P. (2016). Automatic classification of flying bird species using computer vision techniques. Pattern Recognition Letters, 81, 53–62.

    CrossRef  Google Scholar 

  113. Crouse, D., Jacobs, R. L., Richardson, Z., Klum, S., Jain, A., Baden, A. L., et al. (2017). LemurFaceID: A face recognition system to facilitate individual identification of lemurs. BMC Zoology, 2(1), 2.

    CrossRef  Google Scholar 

  114. Krüger, B., Yasin, H., Zsoldos, R., & Weber, A. (2014). Retrieval, recognition and reconstruction of quadruped motions. In Proceedings of International Conference on Computer Graphics Theory and Applications (GRAPP) (pp. 1–8).

    Google Scholar 

  115. Song, D., Qin, N., Xu, Y., Kim, C. Y., Luneau, D., & Goldberg, K. (2008). System and algorithms for an autonomous observatory assisting the search for the ivory-billed woodpecker. In Proceedings of IEEE International Conference on Automation Science and Engineering, 2008. CASE (pp. 200–205).

    Google Scholar 

  116. Zhang, J., Xu, Q., Cao, X., Yan, P., & Li, X. (2014). Hierarchical incorporation of shape and shape dynamics for flying bird detection. Neurocomputing, 131, 179–190.

    CrossRef  Google Scholar 

  117. Yoshihashi, R., Kawakami, R., Iida, M., & Naemura, T. (2015, September). Construction of a bird image dataset for ecological investigations. In Proceedings of IEEE International Conference on Image Processing (ICIP) (pp. 4248–4252).

    Google Scholar 

  118. Weinstein, B. G., & Graham, C. H. (2017). Persistent bill and corolla matching despite shifting temporal resources in tropical hummingbird-plant interactions. Ecology Letters, 20(3), 326–335.

    CrossRef  Google Scholar 

  119. Zeppelzauer, M. (2013). Automated detection of elephants in wildlife video. EURASIP Journal on Image and Video Processing, 2013(1), 46.

    CrossRef  Google Scholar 

  120. Ardovini, A., Cinque, L., & Sangineto, E. (2008). Identifying elephant photos by multi-curve matching. Pattern Recognition, 41(6), 1867–1877.

    MATH  CrossRef  Google Scholar 

  121. Manohar, N., Subrahmanya, S., Bharathi, R. K., YH, S.K. and Kumar, H., (2016, August). Recognition and classification of animals based on texture features through parallel computing. In Proceedings of 2nd IEEE International Conference on Cognitive Computing and Information Processing (CCIP) (pp. 1–5).

    Google Scholar 

  122. Suseethra, S., Chandy, D. A., & Mangai, N. S. (2014, February). Recognition of elephants in infrared images using mean-shift segmentation. In 2014 International Conference on Information Communication and Embedded Systems (ICICES) (pp. 1–6). New York: IEEE.

    Google Scholar 

  123. Hiby, L., Lundberg, T., Karlsson, O., Watkins, J., Jüssi, M., Jüssi, I., et al. (2007). Estimates of the size of the Baltic grey seal population based on photo-identification data. NAMMCO scientific publications, 6, 163–175.

    CrossRef  Google Scholar 

  124. Vu, E. T., Mazurek, M. E., & Kuo, Y.-C. (1994). Identification of a forebrain motor programming network for the learned song of zebra finches. Journal of Neuroscience, 14(11), 6924–6934.

    Google Scholar 

  125. Zhao, J., Fang, Y., Kang, S., Ruan, B., Xu, J., Dong, G., et al. (2014). Identification and characterization of a new allele for ZEBRA LEAF 2, a gene encoding carotenoid isomerase in rice. South African Journal of Botany, 95, 102–111.

    CrossRef  Google Scholar 

  126. Bercovitch, F. B., Berry, P. S., Dagg, A., Deacon, F., Doherty, J. B., Lee, D. E., et al. (2017). How many species of giraffe are there? Current Biology, 27(4), R136–R137.

    CrossRef  Google Scholar 

  127. Fennessy, J., Winter, S., Reuss, F., Kumar, V., Nilsson, M. A., Vamberger, M., et al. (2017). Response to “How many species of giraffe are there?”. Current Biology, 27(4), R137–R138.

    CrossRef  Google Scholar 

  128. Loos, A., & Kalyanasundaram, T. A. M. (2015, April). Face recognition for great apes: Identification of primates in videos. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1548–1552).

    Google Scholar 

  129. Loos, A., & Ernst, A. (2013). An automated chimpanzee identification system using face detection and recognition. EURASIP Journal on Image and Video Processing, 2013(1), 49.

    CrossRef  Google Scholar 

  130. Whittington, P., Klages, N., Crawford, R., Wolfaardt, A., & Kemper, J. (2005). Age at first breeding of the African Penguin. Ostrich-Journal of African Ornithology, 76(1–2), 14–20.

    CrossRef  Google Scholar 

  131. Sherley, R. B., Burghardt, T., Barham, P. J., Campbell, N., & Cuthill, I. C. (2010). Spotting the difference: Towards fully-automated population monitoring of African penguins Spheniscus demersus. Endangered Species Research, 11(2), 101–111.

    CrossRef  Google Scholar 

  132. Che, R., Sun, Y., Wang, R., & Xu, T. (2014). Transcriptomic analysis of endangered Chinese salamander: Identification of immune, sex and reproduction-related genes and genetic markers. PLoS ONE, 9(1), e87940.

    CrossRef  Google Scholar 

  133. Stevens, J. D. (2007). Whale shark (Rhincodon typus) biology and ecology: A review of the primary literature. Fisheries Research, 84(1), 4–9.

    CrossRef  Google Scholar 

  134. Sequeira, A. M., Mellin, C., Fordham, D. A., Meekan, M. G., & Bradshaw, C. J. (2014). Predicting current and future global distributions of whale sharks. Global Change Biology, 20(3), 778–789.

    CrossRef  Google Scholar 

  135. Rohner, C. A., Richardson, A. J., Marshall, A. D., Weeks, S. J., & Pierce, S. J. (2011). How large is the world’s largest fish? Measuring whale sharks Rhincodon typus with laser photogrammetry. Journal of Fish Biology, 78(1), 378–385.

    CrossRef  Google Scholar 

  136. Chelysheva, E. (2004). A new approach to cheetah identification. CAT NEWS, IUCN/CSG, 41, 27–29.

    Google Scholar 

  137. Kelly, M. J. (2001). Computer-aided photograph matching in studies using individual identification: An example from Serengeti cheetahs. Journal of Mammalogy, 82(2), 440–449.

    CrossRef  Google Scholar 

  138. Long, R. A., MacKay, P., Ray, J., & Zielinski, W. (Eds.). (2012). Noninvasive survey methods for carnivores. Washington, D.C.: Island Press.

    Google Scholar 

  139. Beugeling, T., & Branzan-Albu, A. (2014). Computer vision-based identification of individual turtles using characteristic patterns of their plastrons. In Proceedings of IEEE International Conference on Computer and Robot Vision (CRV) (pp. 203–210).

    Google Scholar 

  140. Eckert, K. L., Bjorndal, K. A., Abreu-Grobois, F. A., & Donnelly, M. (1999). Taxonomy, external morphology, and species identification. Research and Management Techniques for the Conservation of Sea Turtles, 4, 21.

    Google Scholar 

  141. Kamińska, D., & Gmerek A. (August 2012). Automatic identification of bird species: A comparison between kNN and SOM classifiers. In Joint Conference on New Trends in Audio & Video and Signal Processing: Algorithms, Architectures, Arrangements and Applications (NTAV/SPA), Lodz, Poland (pp. 77–82).

    Google Scholar 

  142. Lantsova, E., Voitiuk, T., Zudilova, T. and Kaarna, A., 2016, July. Using low-quality video sequences for fish detection and tracking. In Proceedings of IEEE International Conference on SAI Computing Conference (SAI) (pp. 426–433).

    Google Scholar 

  143. Hossain, E., Alam, S. S., Ali, A. A., & Amin, M. A. (2016). Fish activity tracking and species identification in underwater video. In Proceedings of 5th IEEE International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 62–66).

    Google Scholar 

  144. Chen, H. H. (2003). A feasibility study of using color indexing for reef fish identification. Proceedings of IEEE International Conference on OCEANS, 5, 2566.

    Google Scholar 

  145. Ali-Gombe, A., Elyan, E., & Jayne, C. (2017). Fish classification in context of noisy images. In Proceedings of International Conference on Engineering Applications of Neural Networks (pp. 216–226).

    Google Scholar 

  146. Chuang, M. C., Hwang, J. N., Williams, K. (2014). Supervised and unsupervised feature extraction methods for underwater fish species recognition. In Proceedings of IEEE International Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI), pp. 33–40.

    Google Scholar 

  147. Chuang, M. C., Hwang, J. N., & Williams, K. A. (2016). Feature learning and object recognition framework for underwater fish images. IEEE Transactions on Image Processing, 25, 1862–1872.

    MathSciNet  Google Scholar 

  148. Kitchen-Wheeler, A. M. (2010). Visual identification of individual manta ray (Manta alfredi) in the Maldives Islands. Western Indian Ocean. Marine Biology Research, 6(4), 351–363.

    Google Scholar 

  149. Ashour, H., & Sasi, S. (2015). Recognition of stonefish from underwater video. In Proceedings of International Conference on Advances in Computing Communications and Informatics (ICACCI) (pp. 1031–1036).

    Google Scholar 

  150. Liu, C., & Wechsler, H. (2003). Independent component analysis of Gabor features for face recognition. IEEE Transactions on Neural Networks, 14(4), 919–928.

    CrossRef  Google Scholar 

  151. Baba, M., Pescaru, D., Gui, V., & Jian, I. (2016). Stray dogs behavior detection in urban area video surveillance streams. In Proceedings of 12th IEEE International conference on Symposium Electronics and Telecommunications (ISETC) (pp. 313–316).

    Google Scholar 

  152. Toma, D. P., White, K. P., Hirsch, J., & Greenspan, R. J. (2002). Identification of genes involved in Drosophila melanogaster geotaxis, a complex behavioral trait. Nature Genetics, 31(4), 349.

    CrossRef  Google Scholar 

  153. Matarić, M. J. (1995). Designing and understanding adaptive group behavior. Adaptive Behavior, 4(1), 51–80.

    CrossRef  Google Scholar 

  154. Jill, M. Lafleur, Buler, J. J., & Frank, R. Moore. (2016). Geographic position and landscape composition explain regional patterns of migrating landbird distributions during spring stopover along the northern coast of the Gulf of Mexico. Landscape Ecology, 31(8), 1697–1709.

    CrossRef  Google Scholar 

  155. Zhou, H., Yan, C., & Huang, H. (2016). Tree species identification based on convolutional neural networks. In Proceedings of 8th IEEE International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (Vol. 2, pp. 103–106).

    Google Scholar 

  156. Fan, J., Zhou, N., Peng, J., & Gao, L. (2015). Hierarchical learning of tree classifiers for large-scale plant species identification. IEEE Transactions on Image Processing, 24(11), 4172–4184.

    MathSciNet  CrossRef  Google Scholar 

  157. Cohen, C. J., Haanpaa, D., & Zott, J. P. (2015). Machine vision algorithms for robust animal species identification. In Proceedings of IEEE International Conference on Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1–7).

    Google Scholar 

  158. Stern, U., He, R., & Yang, C. H. (2015). Analyzing animal behavior via classifying each video frame using convolutional neural networks. Scientific Reports, 5 (Article number: 14351). https://doi.org/10.1038/srep14351.

  159. Neethirajan, S. (2017). Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research, 12, 15–29.

    CrossRef  Google Scholar 

  160. Liang, Y., Crnic, L., Kobla, V., & Wolf, W. (2004). System and method for object identification and behavior characterization using video analysis. U.S. Patent 6,678,413.

    Google Scholar 

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

    Google Scholar 

  162. 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.

    CrossRef  Google Scholar 

  163. Zhang, B., Gao, Y., Zhao, S., & Liu, J. (2010). Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 19(2), 533–544.

    MathSciNet  MATH  CrossRef  Google Scholar 

  164. 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.

    MATH  CrossRef  Google Scholar 

  165. Bay, H., Tuytelaars, T., & Van Gool, L. (2006). Surf: Speeded up robust features. In Proceedings of IEEE International Conference on Computer Vision–ECCV (pp. 404–417).

    Google Scholar 

  166. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.

    CrossRef  Google Scholar 

  167. Lowe, D. G. (1999). Object recognition from local scale-invariant features. Proceedings of the 7thIEEE International Conference on Computer Vision, 2, 1150–1157.

    Google Scholar 

  168. Li, Q., Wang, G., Liu, J., & Chen, S. (2009). Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2), 287–291.

    CrossRef  Google Scholar 

  169. Maltoni, D., Maio, D., Jain, A., & Prabhakar, S. (2009). Handbook of fingerprint recognition. Berlin: Springer Science & Business Media.

    MATH  CrossRef  Google Scholar 

  170. Prabhakar, S., Pankanti, S., & Jain, A. K. (2003). Biometric recognition: Security and privacy concerns. IEEE Security and Privacy, 99(2), 33–42.

    CrossRef  Google Scholar 

  171. Kumar, S., Tiwari, S., & Singh, S. K. (2015). Face recognition for cattle. In Proceedings of 3rd IEEE International Conference on Image Information Processing (ICIIP) (pp. 65–72).

    Google Scholar 

  172. Clarke, M. (1990). The control of insurance fraud: A comparative view. The British Journal of Criminology, 30(1), 1–23.

    MathSciNet  CrossRef  Google Scholar 

  173. Giraldo-Zuluaga, J. H., Salazar, A., & Daza, J. M. (2016). Semi-supervised recognition of the Diploglossus millepunctatus lizard species using artificial vision algorithms. arXiv preprint arXiv:1611.02803.

  174. Taigman, Y., Wolf, L., & Hassner, T. (2009). Multiple one-shots for utilizing class label information. BMVC, 2, 1–12.

    Google Scholar 

  175. Sun, J., Fan, G., Yu, L., & Wu, X. (2014). Concave-convex local binary features for automatic target recognition in infrared imagery. EURASIP Journal on Image and Video Processing, 2014(1), 23.

    CrossRef  Google Scholar 

  176. A feature descriptor based on local normalized difference for real-world texture classification,” in IEEE Transactions on Multimedia (no. 99), 1–1.

    Google Scholar 

  177. Krause, J., Krause, S., Arlinghaus, R., Psorakis, I., Roberts, S., & Rutz, C. (2013). Reality mining of animal social systems. Trends in Ecology & Evolution, 28(9), 541–551.

    CrossRef  Google Scholar 

  178. Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., et al. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052–19059.

    CrossRef  Google Scholar 

  179. Schick, R. S., Loarie, S. R., Colchero, F., Best, B. D., Boustany, A., Conde, D. A., et al. (2008). Understanding movement data and movement processes: Current and emerging directions. Ecology Letters, 11(12), 1338–1350.

    CrossRef  Google Scholar 

  180. Swan, G. J., Redpath, S. M., Bearhop, S., & McDonald, R. A. (2017). Ecology of problem individuals and the efficacy of selective wildlife management. Trends in Ecology & Evolution.

    Google Scholar 

  181. Huijser, M. P., & McGowen, P. T. (2003). Overview of animal detection and animal warning systems in North America and Europe. Road Ecology Center.

    Google Scholar 

  182. Wichmann, F. A., Drewes, J., Rosas, P., & Gegenfurtner, K. R. (2010). Animal detection in natural scenes: Critical features revisited. Journal of Vision, 10(4), 6.

    CrossRef  Google Scholar 

  183. Zeppelzauer, M., & Stoeger, A. S. (2015). Establishing the fundamentals for an elephant early warning and monitoring system. BMC Research Notes, 8(1), 409.

    CrossRef  Google Scholar 

  184. Van Raamsdonk, L. W. D., Von Holst, C., Baeten, V., Berben, G., Boix, A., & De Jong, J. (2007). New developments in the detection and identification of processed animal proteins in feeds. Animal Feed Science and Technology, 133(1), 63–83.

    CrossRef  Google Scholar 

  185. Ross, A. A., Nandakumar, K., & Jain, A. (2006). Handbook of multibiometrics (Vol. 6). Berlin: Springer Science & Business Media.

    Google Scholar 

  186. Jiménez-Gamero, I., Dorado, G., Muñoz-Serrano, A., Analla, M., & Alonso-Moraga, A. (2006). DNA microsatellites to ascertain pedigree-recorded information in a selecting nucleus of Murciano-Granadina dairy goats. Small Ruminant Research, 65(3), 266–273.

    CrossRef  Google Scholar 

  187. Wang, Z., Fan, B., & Wu, F. (2011, November). Local intensity order pattern for feature description. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 603–610).

    Google Scholar 

  188. Jain, A. K. (1989). Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice-Hall Inc.

    MATH  Google Scholar 

  189. http://sloop.mit.edu/blog/sloop-algorithms-whale-shark-matching.

  190. Shyam, R., & Singh, Y. N. (2015). Face recognition using augmented local binary pattern and Bray Curtis dissimilarity metric. In Proceedings of IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 779–784).

    Google Scholar 

  191. Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635–1650.

    MathSciNet  MATH  CrossRef  Google Scholar 

  192. Wolf, L., Hassner, T., & Taigman, Y. (2011). Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10), 1978–1990.

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Santosh Kumar .

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, S., Singh, S.K., Singh, R., Singh, A.K. (2017). Analytical Study of Animal Biometrics: A Technical Survey. In: Animal Biometrics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7956-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7956-6_2

  • 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)