Skip to main content
SpringerLink
Log in

Cattle Identification from Muzzle Print Image Pattern Using Hybrid Feature Descriptors and SVM

  • Conference paper
  • First Online:
Machine Learning and Big Data Analytics (ICMLBDA 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 401))

Included in the following conference series:

  • 442 Accesses

Abstract

Biometric-based recognition is gaining popularity based on its applications and uses. Identification system in animals is applied on the biometric features, visual features, phenotype traits, and genotype traits. The muzzle point image pattern is a primary biometric feature for the identification of the individual cattle. This feature is very similar to minutiae points of human fingerprints. The study presents the individual cattle identification based on the muzzle image pattern of cattle. The identification is required for the verification of false insurance claims, registration of cattle, and traceability of natural habitat of cattle. The proposed system uses the texture feature descriptor, scale-invariant, and speeded up robust to extract features from the muzzle point images pattern. By using muzzle image pattern database of 930 images, this proposed algorithm yields the desired results of 90.22% identification accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
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

Similar content being viewed by others

References

  1. 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 

  2. Bakour, K., & Ãœnver, H. M. (2021). VisDroid. Android malware classification based on local and global image features, bag of visual words and machine learning techniques. Neural Computing and Applications, 33(8):3133-3153.

    Article  Google Scholar 

  3. Baranov, A.S., Graml, R., Pirchner, F., et al. (1993). Breed differences and intrabreed genetic variability of dermatoglyphic pattern of cattle, Animal Breeding Genetics, 110, (1–6):385–392.

    Article  Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. Bay H, Tuytelaars T, & Van Gool L (2006). Surf: Speeded up robust features. European conference on computer vision, 404-417.

    Google Scholar 

  6. Bello, R. W., Olubummo, D. A., Seiyaboh, Z., Enuma, O. C., Talib, A. Z., & Mohamed, A. S. A. (2020). Cattle identification: the history of nose prints approach in brief. Proceeding of international Conference Series: Earth and Environmental Science 594(1): 012026.

    Google Scholar 

  7. Jang D. H., Kwon K. S., Kim J. K., Yang K. Y., & Kim, J. B. (2020). Dog Identification Method Based on Muzzle Pattern Image. Applied Sciences, 10(24):8994.

    Article  Google Scholar 

  8. Kabbai, L., Abdellaoui, M., & Douik, A. (2019). Image classification by combining local and global features. The Visual Computer, 35(5):679-693.

    Article  Google Scholar 

  9. Kaur, A., Kumar, M., & Jindal, M. K. (2022). Shi-Tomasi corner detector for cattle identification from muzzle print image pattern. Ecological Informatics, 101549. https://doi.org/10.1016/j.ecoinf.2021.101549

  10. Kumar, S., Chaube, M. K., & Kumar, S. (2021). Secure and Sustainable Framework for Cattle Recognition Using Wireless Multimedia Networks and Machine Learning Techniques. IEEE Transactions on Sustainable Computing.

    Google Scholar 

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

    Article  Google Scholar 

  12. Kumar, S., Singh, S. K., Abidi, A. I., Datta, D., & Sangaiah, A. K. (2018). Group sparse representation approach for recognition of cattle on muzzle point images. International Journal of Parallel Programming, 46(5):812-837.

    Article  Google Scholar 

  13. Kumar, S., Singh, S. K., & Singh, A. K. (2017). Muzzle point pattern-based techniques for individual cattle identification. IET Image Processing, 11(10): 805-814.

    Article  Google Scholar 

  14. Kusakunniran W, Wiratsudakul A, Chuachan U, Kanchanapreechakorn S, Imaromkul T, Suksriupatham N, & Thongkanchorn K. (2020). Biometric for Cattle Identification using Muzzle Patterns. International Journal of Pattern Recognition and Artificial Intelligence, 2056007.

    Google Scholar 

  15. Kusakunniran, W., Wiratsudakul, A., Chuachan, U., Imaromkul, T., Kanchanapreechakorn, S., Suksriupatham, N., & Thongkanchorn, K. (2021). Analysing muzzle pattern images as a biometric for cattle identification. International Journal of Biometrics, 13(4): 367-384.

    Article  Google Scholar 

  16. 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 

  17. Mishra S., Tomer O.S., Kalm E. (1995). Muzzle dermatoglyphics: a new method to identify bovines. Asian Livestock, 91–96.

    Google Scholar 

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

    Article  Google Scholar 

  19. Nurtanio I, Areni IS, Bugiwati SR, Bustamin A & Rahmatullah M (2020). A Portable Cattle Tagging Based on Muzzle Pattern. International Journal of Interactive Mobile Technologies, 14(13).

    Google Scholar 

  20. Rublee E., Rabaut V., Konolige K, Bradski G. (2011) ORB: an efficient alternative to SIFT or SURF, Proceedings of the IEEE International Conference on Computer Vision, 2564-2571.

    Google Scholar 

  21. Sian C, Jiye W, Ru Z, & Lizhi Z (2020). Cattle identification using muzzle print images based on feature fusion. Proceeding of Conference Series: Materials Science and Engineering 853(1):012051

    Google Scholar 

  22. Singh, P., Devi, K. J., & Varish, N. (2021). Muzzle Pattern Based Cattle Identification Using Generative Adversarial Networks. In Soft Computing for Problem Solving, 13-23.

    Google Scholar 

  23. Shojaeipour, A., Falzon, G., Kwan, P., Hadavi, N., Cowley, F. C., & Paul, D. (2021). Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle. Agronomy, 11(11):2365.

    Article  Google Scholar 

  24. Tharwat A, Gaber T, & Hassanien, A.E. (2015). Two biometric approaches for cattle identification based on features and classifiers fusion. International Journal of Image Mining, 1(4):342-365.

    Google Scholar 

  25. Vapnik, V., Golowich, S. E., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, 281-287.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Applications, Guru Nanak College, Sri Muktsar Sahib, Punjab, India

    Amanpreet Kaur

  2. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  3. Department of Computer Science and Applications, Panjab University Regional Centre, Sri Muktsar Sahib, Punjab, India

    M. K. Jindal

Authors
  1. Amanpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. K. Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Rajiv Misra

  2. Cardiff University, Cardiff, UK

    Rana Omer

  3. Dept. of EE Engineering, University of London, London, UK

    Muttukrishnan Rajarajan

  4. Dept. of ECE, National University of Singapore, Singapore, Singapore

    Bharadwaj Veeravalli

  5. Dept. of Computer Science, Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  6. Dept. of CSE, Indian Institute of Information Technology, Kota, Jawahar Lal Nehru Marg, Rajasthan, India

    Priyanka Mishra

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kaur, A., Kumar, M., Jindal, M.K. (2023). Cattle Identification from Muzzle Print Image Pattern Using Hybrid Feature Descriptors and SVM. In: Misra, R., Omer, R., Rajarajan, M., Veeravalli, B., Kesswani, N., Mishra, P. (eds) Machine Learning and Big Data Analytics. ICMLBDA 2022. Springer Proceedings in Mathematics & Statistics, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-031-15175-0_39

Download citation

Publish with us

Policies and ethics

Search

Navigation