Deep Belief Network Approach for Recognition of Cow using Cow Nose Image Pattern


  • Rotimi-Williams BELLO School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Abdullah Zawawi Hj TALIB School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
  • Ahmad Sufril Azlan Bin MOHAMED School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia



Animal biometrics, Cow nose image, Convolutional neural network, Deep belief network, Identification


A deep belief network is proposed to learn the discriminatory cow nose image texture features for a robust representation of cows' features and recognition using a cow nose image pattern. Deep belief network is a deep learning model that is graphically based, and it is applied to learn the extracted feature sets of cow nose image pattern for hierarchical representation by using the training details of the training phase of the system proposed. Deep belief network application is useful in animal biometrics to monitor the animals through its recognition and identification techniques. Biometrics application emanated from computer vision and pattern recognition. Its application plays an important role in registering and monitoring animals through its recognition and identification techniques. Because the existing physical-based feature representation methods and manual visual feature extractions cannot handle animal recognition, the deep belief network technique is proposed using the animal's visual attributes. An experiment performed under a controlled condition of identification indicated that the proposed method outshines the existing methods with approximately 98.99 % accuracy. Four thousand cow nose images from an existing database of 400 individual cows contribute to the community of research, especially in the animal biometrics for identification of individual cow.


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M Vlad, RA Parvulet and MS Vlad. A survey of livestock identification systems. In: Proceedings of the 13th WSEAS International Conference on Automation and Information. 2012, p. 165-70.

CM Roberts. Radio frequency identification (RFID). Comput. Secur. 2006; 25, 18-26.

Z Wang, Z Fu, W Chen and J Hu. A RFID-based traceability system for cattle breeding in china. In: Proceedings of the 2010 IEEE International Conference on Computer Application and System Modeling, Taiyuan, China, 2010, p. V2-567.

A Krizhevsky, I Sutskever and G Hinton. Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems. Curran and Associates, Red Hook, NY, USA, 2012, p. 1097-105.

C Farabet, C Couprie, L Najman and Y LeCun. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mac. Intell. 2013; 35, 1915-29.

Y Sun, X Wang and X Tang. Deep convolutional network cascade for facial point detection. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2013; 2013, 3476-83.

S Kumar and SK Singh. Visual animal biometrics: Survey. IET Biometrics 2016; 6, 139-56.

S Kumar, SK Singh, T Datta and HP Gupta. A fast cattle recognition system using smart devices. In: Proceedings of the 2016 ACM conference on Multimedia, Amsterdam, Netherlands, 2016, p. 742-3.

UG Barron, F Butler, K McDonnell and S Ward. The end of the identity crisis? Advances in biometric markers for animal identification. Irish Veterinary J. 2009; 62, 204-8.

AK Jain, AA Ross and K Nandakumar. Introduction to biometrics. Springer Science and Business Media, 2011.

R Giot, M El-Abed and C Rosenberger. Fast computation of the performance evaluation of biometric systems: Application to multibiometrics. Future Generat. Comput. Syst. 2013; 29, 788-99.

AK Jain, A Ross and S Prabhakar. An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Tech. 2004; 14, 4-20.

WE Petersen. The identification of the bovine by means of nose-prints. J. Dairy Sci. 1922; 5, 249-58.

HS Kohl and T Burkhart. Animal biometrics: Quantifying and detecting phenotypic appearance. Trends Ecol. Evol. 2013; 28, 432-41.

S Reiter, G Sattlecker, L Lidauer, F Kickinger, M Öhlschuster, W Auer, V Schweinzer, D Klein-Jöbstl, M Drillich and M Iwersen. Evaluation of an ear-tag-based accelerometer for monitoring rumination in dairy cows. J. Dairy Sci. 2018; 101, 3398-411.

C Seijas, G Montilla and L Frassato. Identification of Rodent Species Using Deep Learning. Computación y Sistemas. 2019; 23, 257.

MF Hansen, ML Smith, LN Smith, MG Salter, EM Baxter, M Farish and B Grieve. Towards on-farm pig face recognition using convolutional neural networks. Comput. Ind. 2018; 98, 145-52.

S Kumar and SK Singh. Cattle recognition: A new frontier in visual animal biometrics research. In: Proceedings of the National Academy of Sciences. India, 2019, p. 1-20.

MS Norouzzadeh, A Nguyen, M Kosmala, A Swanson, MS Palmer, C Packer and J Clune. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci Unit States Am. 2018; 115, E5716-E5725.

TT Zin, CN Phyo, P Tin, H Hama and I Kobayashi. Image technology based cow identification system using deep learning. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2018.

S Kumar, A Pandey, KSR Satwik, S Kumar, SK Singh, AK Singh and A Mohan. Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 2018; 116, 1-17.

IA Iswanto and B Li. Visual object tracking based on mean-shift and particle-Kalman filter. Proc. Comput. Sci. 2017; 116, 587-95.

A Noviyanto and AM Arymurthy. Automatic cattle identification based on muzzle photo using

speed-up robust features approach. In: Proceedings of the 3rd European Conference of Computer

Science. 2012, p. 114.

A Nasirahmadi, U Richter, O Hensel, S Edwards and B Sturm. Using machine vision for investigation of changes in pig group lying patterns. Comput. Electron. Agr. 2015; 119, 184-90.

H Minagawa, T Fujimura, M Ichiyanagi, K Tanaka and M Fangquan. 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 Asian Agricultural Information Technology and Management. 2002, p. 596-600.

B Barry, U Gonzales-Barron, K McDonnell, F Butler and S Ward. Using muzzle pattern recognition as a biometric approach for cattle identification. Trans. ASABE 2007; 50, 1073-80.

N Dalal and B Triggs. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, p. 886-93.

AI Awad, HM Zawbaa, HA Mahmoud, EHHA Nabi, RH Fayed and AE Hassanien. A robust cattle identification scheme using muzzle print images. In: Proceedings of IEEE Federated Conference on Computer Science and Information Systems. 2013, p. 529-34.

A Noviyanto and AM Arymurthy. Beef cattle identification based on muzzle pattern using a matching refinement technique in the sift method. Comp. Electr. Agr. 2013; 99, 77-84.

K Ehsani, H Bagherinezhad, J Redmon, R Mottaghi and A Farhadi. Who let the dogs out? Modeling dog behavior from visual data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018, p. 4051- 60.

S Kumar, S Tiwari and SK Singh. Face recognition for cattle. In: Proceedings of 3rd IEEE International Conference on Image Information Processing. 2015, p. 65-72.

T Gaber, A Tharwat, AE Hassanien and V Snasel. Biometric cattle identification approach based on webers local descriptor and AdaBoost classifier. Comp. Electr. Agr. 2016; 122, 55-66.

KP Risha, KA Chempak and CS Sindhu. Difference of gaussian on frame differenced image. Int. J. Innovat. Res. Electr. Electron. Instrum. Contr. Eng. 2016; 3, 92-5.

P Vincent, H Larochelle, I Lajoie, Y Bengio and PA Manzagol. Stacked denoising auto-encoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 2010; 11, 3371-408.

P Vincent, H Larochelle, Y Bengio and PA Manzagol. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning. 2008, p. 1096-103.

Y Bengio. Learning deep architectures for AI. Found. Trends Mach. Learn. 2009; 2, 1-127.

Y Bengio, A Courville and P Vincent. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 2013; 35, 1798-828.




How to Cite

BELLO, R.-W. ., TALIB, A. Z. H. ., & MOHAMED, A. S. A. B. . (2021). Deep Belief Network Approach for Recognition of Cow using Cow Nose Image Pattern. Walailak Journal of Science and Technology (WJST), 18(5), Article 8984 (14 pages).