Skip to main content

Real-Time Recognition of Cattle Using Fisher Locality Preserving Projection Method

  • Chapter
  • First Online:
Animal Biometrics
  • 496 Accesses

Abstract

With the arrival of adequate computer vision techniques, animal biometrics-based recognition systems have accomplished attention for the identification and monitoring of jeopardized species and individual animal. In this chapter, a novel fisher locality preserving projection-based cattle recognition framework is proposed for extraction and representation of cattle identification in real time. The biometric muzzle point image of cattle is captured using the surveillance camera and transferred them to the server of cattle recognition framework by using wireless network technology. The motivation of proposed method is to maximize the inter-class (between-class) scatter feature matrix of the muzzle point image and efficiently minimize the intra-class (within-class) scatter matrix of muzzle point images. This strategy of proposed method improves the accuracy of cattle identification. The efficacy of proposed recognition approach for cattle is estimated under different identification settings. The proposed method yields 96.87% recognition rate for identifying individual cattle. Further, the method assessed the 10.25 recognition time (seconds) for enrollment and recognition of biometrics muzzle point feature for cattle on the different image database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hilton-Taylor, C., Stuart, S. N. (2009). Wildlife in a Changing World—An Analysis of the 2008 IUCN Red List of Threatened Species. IUCN: Gland Switzerland. http://www.iucnredlist.org/technical-documents/references.

  2. Walsh, P. D., Abernethy, K. A., Bermejo, M., & Beyers, R. (2003). Catastrophic ape decline in western equatorial Africa. Nature, 422(6932), 611.

    Article  Google Scholar 

  3. Campbell, G., Kuehl, H., Kouamé, P. N. G., & Boesch, C. (2008). Alarming decline of West African chimpanzees in Côte d’Ivoire. Current Biology, 18(19), R903–R904.

    Article  Google Scholar 

  4. T. H. S. of the United States, Pets by the numbers. http://www.humanesociety.org.

  5. Valentin, G. (2014). Gestural activity recognition for canine-human communication. In Proceedings of the 2014 ACM International Symposium on Wearable Computers: Adjunct Program (pp. 145–149). ACM.

    Google Scholar 

  6. Pets population. http://www.slate.com/articles/technology. Retrieved: October 23, 2017.

  7. Is there room for pets in smart cities? https://pacomaroto.wordpress.com/smart-cities-series/is-there-roomfor-pets-in-smart-cities/. Retrieved: June 30, 2016.

  8. Pet animal population kernel description. https://pacomaroto.wordpress.com/. Retrieved: June 30, 2016.

  9. Pet animal adaptation. https://www.columbus.gov/Residents/Animalsand-Pets.htm. Retrieved: June 29, 2016.

  10. Johnston, A., & Edwards, D. (1996). Welfare implications of identification of cattle by ear tags. The Veterinary Record, 138(25), 612–614.

    Article  Google Scholar 

  11. Kumar, S., & Singh, S. K. (2014). Biometric recognition for pet animal. Journal of Software Engineering and Applications, 7(5), 470–482.

    Article  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. Kühl, H. S., & Burghardt, T. (2013). Animal biometrics: Quantifying and detecting phenotypic appearance. Trends in Ecology & Evolution, 28(7), 432–441.

    Article  Google Scholar 

  14. Pasquaretta, C., Levé, M., Claidiere, N., Van De Waal, E., Whiten, A., MacIntosh, A. J., et al. (2014). Social networks in primates: Smart and tolerant species have more efficient networks. Scientific Reports, 4, 7600.

    Article  Google Scholar 

  15. Botella, G., & García, C. (2016). Real-time motion estimation for image and video processing applications. Journal of Real-Time Image Processing, 11(4), 625–631.

    Article  Google Scholar 

  16. Holdgate, M. R. (2015). Applying GPS and accelerometers to the study of African Savanna (Loxodonta africana) and Asian elephant (Elephas maximus) welfare in zoos (Doctoral dissertation, Portland State University).

    Google Scholar 

  17. Yang, Y., Yang, J., Liu, L., & Wu, N. (2017). High-speed target tracking system based on a hierarchical parallel vision processor and gray-level LBP algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(6), 950–964.

    Article  Google Scholar 

  18. Nam, Y., & Hong, S. (2015). Real-time abnormal situation detection based on particle advection in crowded scenes. Journal of Real-Time Image Processing, 10(4), 771–784.

    Article  Google Scholar 

  19. Deng, Y., & Manjunath, B. S. (2001). Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8), 800–810.

    Article  Google Scholar 

  20. Iloanusi, O. N. (2017). Effective statistical-based and dynamic fingerprint preprocessing technique. IET Biometrics, 6(1), 9–18, 1. https://doi.org/10.1049/iet-bmt.2015.0064.

  21. Ekinci, M., & Aykut, M. (2007). A novel approach for automatic palmprint recognition. Lecture Notes in Computer Science, 4509, 122–133.

    Article  MathSciNet  Google Scholar 

  22. Liu, Q., Lu, H., & Ma, S. (2004). Improving kernel Fisher discriminant analysis for face recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 42–49.

    Article  Google Scholar 

  23. Baudat, G., & Anouar, F. (2000). Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10), 2385–2404.

    Article  Google Scholar 

  24. He, X., & Niyogi, P. (2004). Locality preserving projections. In Advances in neural information processing systems (pp. 153–160).

    Google Scholar 

  25. Aldhahab, A., & Mikhael, W. B. (2017). Face recognition employing DMWT followed by FastICA. Circuits, Systems, and Signal Processing, 1–29.

    Google Scholar 

  26. Yan, S., Xu, D., Zhang, B., Zhang, H. J., Yang, Q., & Lin, S. (2007). Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1), 40–51.

    Article  Google Scholar 

  27. Yang, J., Zhang, D., Yang, J. Y., & Niu, B. (2007). Globally maximizing, locally minimizing: Unsupervised discriminant projection with applications to face and palm biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 650–664.

    Article  Google Scholar 

  28. Yu, W., Teng, X., & Liu, C. (2006). Face recognition using discriminant locality preserving projections. Image and Vision Computing, 24(3), 239–248.

    Article  Google Scholar 

  29. Lai, Z. R., Dai, D. Q., Ren, C. X., & Huang, K. K. (2015). Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Transactions on Image Processing, 24(6), 1735–1747.

    Article  MathSciNet  Google Scholar 

  30. Chen, G., Yang, K., Chen, L., Gao, Y., Zheng, B., & Chen, C. (2017). Metric similarity joins using MapReduce. IEEE Transactions on Knowledge and Data Engineering, 29(3), 656–669.

    Article  Google Scholar 

  31. Wolf, L., Hassner, T., & Taigman, Y. (2009). The one-shot similarity kernel. In Proceedings of 12th IEEE International Conference on Computer Vision (pp. 897–902).

    Google Scholar 

  32. Tangruamsub, S., Takada, K., & Hasegawa, O. (2012). A fast online incremental learning method for object detection and pose classification using voting and combined appearance modeling. Signal Processing: Image Communication, 27(1), 75–82.

    Google Scholar 

  33. Ghanbari, E., & Beigy, H. (2015). Incremental RotBoost algorithm: An application for spam filtering. Intelligent Data Analysis, 19(2), 449–468.

    Google Scholar 

  34. Kumar, S., Singh, S. K., Singh, R. S., Singh, A. K., & Tiwari, S. (2017). Real-time recognition of cattle using animal biometrics. Journal of Real-Time Image Processing, 13(3), 505–526.

    Article  Google Scholar 

  35. Sharma, M., Hebbalaguppe, R., & Vig, L. (2017). Pre-trained classifiers with One Shot Similarity for context aware face verification and identification. In Proceedings of IEEE 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) (pp. 1–7).

    Google Scholar 

  36. Yang, L., & Jin, R. (2006). Distance metric learning: A comprehensive survey. Michigan State Universiy 2(2).

    Google Scholar 

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

    Article  Google Scholar 

  38. 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.

    Article  MATH  Google Scholar 

  39. Ojala, T., Pietikäinen, M., & Mäenpää, 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.

    Article  MATH  Google Scholar 

  40. Turk, M. A., & Pentland, A. P. (1991). Face recognition using eigenfaces. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’91) (pp. 586–591).

    Google Scholar 

  41. Etemad, K., & Chellappa, R. (1997). Discriminant analysis for recognition of human face images. JOSA A, 14(8), 1724–1733.

    Article  Google Scholar 

  42. Hastie, T., & Tibshirani, R. (1996). Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 607–616.

    Article  Google Scholar 

  43. Lee, T. W. (1998). Independent component analysis. Independent component analysis (pp. 27–66). US: Springer.

    Chapter  Google Scholar 

  44. Pang, S., Ozawa, S., & Kasabov, N. (2005). Incremental linear discriminant analysis for classification of data streams. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(5), 905–914.

    Google Scholar 

  45. Uray, M., Skocaj, D., Roth, P. M., Bischof, H., & Leonardis, A. (2007). Incremental LDA learning by combining reconstructive and discriminative approaches. BMVC, 2007, 272–281.

    Google Scholar 

  46. 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.

    Article  Google Scholar 

  47. He, X., Cai, D., Yan, S., & Zhang, H. J. (2005). Neighborhood preserving embedding. Proceedings of Tenth IEEE International Conference on Computer Vision, 2, 1208–1213.

    Google Scholar 

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

    Article  Google Scholar 

  49. Jain, A., Flynn, P., & Ross, A. A. (2008). Handbook of biometrics. Newyork: Springer Science & Business Media. https://doi.org/10.1007/978-0-387-71041-9.

  50. Kumar, S., Datta, D., Singh, S. K., & Sangaiah, A. K. (2017). An intelligent decision computing paradigm for crowd monitoring in the smart city. Journal of Parallel and Distributed Computing.

    Google Scholar 

  51. Haider, K. Z., Malik, K. R., Khalid, S., Nawaz, T., & Jabbar, S. (2017). Deepgender: Real-time gender classification using deep learning for smartphones. Journal of Real-Time Image Processing, 1–15.

    Google Scholar 

  52. Mahale, G., Mahale, H., Nandy, S. K., & Narayan, R. (2016). Refresh: Redefine for face recognition using sure homogeneous cores. IEEE Transactions on Parallel and Distributed Systems, 27(12), 3602–3616.

    Article  Google Scholar 

  53. Jiang, F., Ren, J., Lee, C., Shi, W., Liu, S., & Zhao, D. (2017). Spatial and temporal pyramid-based real-time gesture recognition. Journal of Real-Time Image Processing, 1–13.

    Google Scholar 

  54. Biswas, S. K., & Milanfar, P. (2016). One shot detection with laplacian object and fast matrix cosine similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 546–562.

    Article  Google Scholar 

  55. 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.

    Article  Google Scholar 

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

    Google Scholar 

  57. Budagavi, M. (2006). Real-time image and video processing in portable and mobile devices. Journal of Real-Time Image Processing, 1(1), 3–7.

    Article  Google Scholar 

  58. Han, B., Jia, W., & Lin, L. (2007). Performance evaluation of scheduling in IEEE 802.16 based wireless mesh networks. Computer Communications, 30(4), 782–792.

    Article  Google Scholar 

  59. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892.

    Article  MATH  Google Scholar 

  60. Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277–1294.

    Article  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

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, S., Singh, S.K., Singh, R., Singh, A.K. (2017). Real-Time Recognition of Cattle Using Fisher Locality Preserving Projection Method. In: Animal Biometrics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7956-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7956-6_7

  • 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)

Publish with us

Policies and ethics