Skip to main content
SpringerLink
Log in

Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a better identification of different animal species and individual animals. In this work, we propose an effective approach for automatic cattle recognition based on the multiple features of muzzle points and the cattle face images. The proposed method deals the cattle recognition problem as a classification problem among the multiple linear regression models and provides a new theory for the recognition of individual cattle. The group sparse signal representation based classification offers the key to addressing this problem using L2-minimization. In this paper, a comparative study among the well-established handcrafted texture feature extraction techniques and the appearance-based feature extraction techniques is also presented. A detailed set of experimental results on muzzle point image database is also carried to prove the theory. Our method has achieved 93.87% identification accuracy which demonstrates the superiority of the proposed method than the other existing machine learning based recognition algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

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

  3. Awad, A.I.: From classical methods to animal biometrics: a review on cattle identification and tracking. Comput. Electron. Agric. 123, 423–435 (2016)

    Article  Google Scholar 

  4. Barry, B., Gonzales-Barron, U.A., McDonnell, K., Butler, F., Ward, S.: Using muzzle pattern recognition as a biometric approach for cattle identification. Trans. ASABE 50(3), 1073–1080 (2007)

    Article  Google Scholar 

  5. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  6. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  7. Bharadwaj, S., Bhatt, H.S., Singh, R., Vatsa, M., Noore, A.: QFuse: Online learning framework for adaptive biometric system. Pattern Recogn. 48(11), 3428–3439 (2015)

    Article  Google Scholar 

  8. Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of sift features for face authentication. In: Conference on Computer Vision and Pattern Recognition Workshop, pp. 35–35 (2006)

  9. Cai, C., Li, J.: Cattle face recognition using local binary pattern descriptor. In: Proceedings of IEEE Annual Summit and Conference on Signal and Information Processing Association (APSIPA), 2013 Asia-Pacific, pp. 1–4 (2013)

  10. Duyck, J., Finn, C., Hutcheon, A., Vera, P., Salas, J., Ravela, S.: Sloop: a pattern retrieval engine for individual animal identification. Pattern Recogn. 48(4), 1059–1073 (2015)

    Article  Google Scholar 

  11. Elhamifar, E., Vidal, R.: Robust classification using structured sparse representation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1873–1879 (2011)

  12. Gaber, T., Tharwat, A., Hassanien, A.E., Snasel, V.: Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier. Comput. Electron. Agric. 122, 55–66 (2016)

    Article  Google Scholar 

  13. Goswami, G., Mittal, P., Majumdar, A., Vatsa, M., Singh, R.: Group sparse representation based classification for multi-feature multimodal biometrics. Inf. Fusion 32, 3–12 (2016)

    Article  Google Scholar 

  14. Huang, Z., Liu, Y., Li, C., Yang, M., Chen, L.: A robust face and ear based multimodal biometric system using sparse representation. Pattern Recogn. 46(8), 2156–2168 (2013)

    Article  Google Scholar 

  15. Johnston, A., Edwards, D.: Welfare implications of identification of cattle by ear tags. Vet. Rec. 138(25), 612–614 (1996)

    Article  Google Scholar 

  16. Kühl, H.S., Burghardt, T.: Animal biometrics: quantifying and detecting phenotypic appearance. Trends Ecol. Evol. 28(7), 432–441 (2013)

    Article  Google Scholar 

  17. Kumar S., Singh S.K.: Visual animal biometrics: survey . IET Biometrics, pp. 1–38 (2016) https://doi.org/10.1049/iet-bmt.2016.0017

    Article  Google Scholar 

  18. Kumar, S., Pandey, A., Satwik, K.S.R., Kumar, S., Singh, S.K., Singh, A.K., Mohan, A.: Deep Learning framework for recognition of cattle using muzzle point image pattern. Measurement (2017)

  19. Kumar, S., Singh, S.K., Datta, T., Gupta, H.P.: A fast cattle recognition system using smart devices. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 742–743 (2016)

  20. Kumar, S., Singh, S.K., Dutta, T., Gupta, H.P.: 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 (2016)

  21. Kumar, S., Singh, S., Singh, A.: A muzzle point pattern based techniques for individual cattle identification. IET Image Process. 11(10), 805–814 (2017). https://doi.org/10.1049/iet-ipr.2016.0799

    Article  Google Scholar 

  22. Kumar, S., Singh, S.K.: Monitoring of pet animal in smart cities using animal biometric. Future Gener. Comput. Syst. (2016). https://doi.org/10.1016/j.future.2016.12.006

    Article  Google Scholar 

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

  24. Kumar, S., Singh, S.: Biometric recognition for pet animal. J. Softw. Eng. Appl. 7, 470–482 (2014). https://doi.org/10.4236/jsea.2014.75044

    Article  Google Scholar 

  25. Kumar, S., Singh, S.K.: Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm. Multimedia Tools Appl. 76, 1–30 (2016)

    Google Scholar 

  26. Kumar, S., Tiwari, S., Singh, S.K.: Face recognition of cattle: can it be done? Proc. Natl. Acad. Sci. India, Sect. A 86(2), 137–148 (2016)

    Article  Google Scholar 

  27. Kumar, S., Singh, S.K., Singh, R.S., Singh, A.K., Tiwari, S.: Real-time recognition of cattle using animal biometrics. J. Real-Time Image Proc. 13, 1–22 (2016)

    Google Scholar 

  28. Li, W., Ji, Z., Wang, L., Sun, C., Yang, X.: Automatic individual identification of Holstein dairy cows using tailhead images. Comput. Electron. Agric. 142(B), 622–631 (2017). https://doi.org/10.1016/j.compag.2017.10.029

    Article  Google Scholar 

  29. Li, J., Lu, C.Y.: A new decision rule for sparse representation based classification for face recognition. Neurocomputing 116, 265–271 (2013)

    Article  Google Scholar 

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

  31. Majumdar, A., Ward, R.K.: Robust classifiers for data reduced via random projections. IEEE Trans. Syst. Man Cybern. B Cybern. 40(5), 1359–1371 (2010)

    Article  Google Scholar 

  32. Minagawa, H., Fujimura, T., Ichiyanagi, M., Tanaka, K., Fangquan, M.: Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. In Proceedings of the 3rd Asian Conference for Information Technology in Agriculture, pp. 596–600 (2002)

  33. Noviyanto A., Arymurthy A. M.: Automatic cattle identification based on muzzle photo using speed-up robust features approach. In: Proceedings of the 3rd European Conference of Computer Science, ECCS, Vol. 110, pp. 114 (2012)

  34. Noviyanto, A., Arymurthy, A.M.: Beef cattle identification based on muzzle pattern using a matching refinement technique in the SIFT method. Comput. Electron. Agric. 99, 77–84 (2013)

    Article  Google Scholar 

  35. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  36. Ouyang, Y., Sang, N., Huang, R.: Accurate and robust facial expressions recognition by fusing multiple sparse representation based classifiers. Neurocomputing 149(A), 71–78 (2015)

    Article  Google Scholar 

  37. Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recogn. Lett. 24(13), 2115–2125 (2003)

    Article  Google Scholar 

  38. Shekhar, S., Patel, V.M., Nasrabadi, N.M., Chellappa, R.: Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 113–126 (2014)

    Article  Google Scholar 

  39. Tharwat, A., Gaber, T., Hassanien, A.E., Hassanien, H.A., Tolba, M.F.: 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 (2014)

    Google Scholar 

  40. Tharwat, A., Mahdi, H., El Hennawy, A., Hassanien, A.E.: Face sketch recognition using local invariant features. In: Proceedings of 7th IEEE International Conference on Soft Computing and Pattern Recognition (SoCPaR), pp. 117–122 (2015). https://doi.org/10.1109/SOCPAR.2015.7492793

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

  42. Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM International Conference on Multimedia pp. 1469–1472 (2010)

  43. Wardrope, D.: Problems [suppurating wounds] with the use of ear tags in cattle.[correspondence], Veterinary Record (United Kingdom)

  44. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  45. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  46. Yuan, X.T., Liu, X., Yan, S.: Visual classification with multitask joint sparse representation. IEEE Trans. Image Process. 21(10), 4349–4360 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, IIIT, Naya Raipur, Chhattisgarh, 493661, India

    Santosh Kumar

  2. Department of Computer Science and Engineering, IIT (B.H.U), Varanasi, 221005, India

    Sanjay Kumar Singh, Ali Imam Abidi & Deepanwita Datta

  3. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, 632014, India

    Arun Kumar Sangaiah

Authors
  1. Santosh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Imam Abidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deepanwita Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arun Kumar Sangaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Kumar Sangaiah.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Singh, S.K., Abidi, A.I. et al. Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images. Int J Parallel Prog 46, 812–837 (2018). https://doi.org/10.1007/s10766-017-0550-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-017-0550-x

Keywords

Search

Navigation