Towards on-farm pig face recognition using convolutional neural networks

Computers in Industry

Volumn 98, Issue , 2018, Pages 145-152

  Hansen, Mark F ^a   Smith, Melvyn L ^a   Smith, Lyndon N ^a   Salter, Michael G ^b   Baxter, Emma M ^c   Farish, Marianne ^c   Grieve, Bruce ^d  

^a UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

^b AB Enzymes GmbH   (Germany)

^c SCOTTISH AGRICULTURAL COLLEGE   (United Kingdom)

^d UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Biometrics; Convolutional neural network; Deep learning; Pig face recognition

Indexed keywords

AGRICULTURE; ANIMALS; BIOMETRICS; CONVOLUTION; DEEP LEARNING; MAMMALS; NEURAL NETWORKS; STATISTICAL TESTS;

AUGMENTED DATA SET; BEST PRACTICES; CONVOLUTIONAL NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORKS (CNN); FARM ENVIRONMENTS; HUMAN FACE RECOGNITION; IDENTIFICATION OF INDIVIDUALS; OBJECTIVE MEASUREMENT;

FACE RECOGNITION;

EID: 85044155039     PISSN: 01663615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compind.2018.02.016     Document Type: Article

References (28)

1. Maselyne, J., Saeys, W., De Ketelaere, B., Mertens, K., Vangeyte, J., Hessel, E.F., Millet, S., Van Nuffel, A., Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs. Comput. Electron. Agric. 102:March (Supplement C) (2014), 10–18.
2. Bledsoe, W.W., Man–Machine Facial Recognition. 1966, Panoramic Research Inc., Palo Alto, CA.
3. Turk, M., Pentland, A., Eigenfaces for recognition. J. Cognit. Neurosci. 3:1 (1991), 71–86.
4. 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 (1997), 711–720.
5. Taigman, Y., Yang, M., Ranzato, M., Wolf, L., Deepface: closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, 1701–1708.
6. Parkhi, O.M., Vedaldi, A., Zisserman, A., Deep face recognition. Proceedings of the British Machine Vision Conference (BMVC), vol. 1, 2015, 41.1–41.12.
7. Ahrendt, P., Gregersen, T., Karstoft, H., Development of a real-time computer vision system for tracking loose-housed pigs. Comput. Electron. Agric. 76:May (2) (2011), 169–174.
8. Porto, S.M.C., Arcidiacono, C., Anguzza, U., Cascone, G., A computer vision-based system for the automatic detection of lying behaviour of dairy cows in free-stall barns. Biosyst. Eng. 115:2 (2013 June), 184–194.
9. Porto, S.M.C., Arcidiacono, C., Anguzza, U., Cascone, G., The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system. Biosyst. Eng. 133:May (Supplement C) (2015), 46–55.
10. Arcidiacono, C., Porto, S.M.C., Mancino, M., Cascone, G., Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Comput. Electron. Agric. 134:March (Supplement C) (2017), 124–134.
11. 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:March (Supplement C) (2016), 55–66.
12. Kumar, S., Singh, S.K., Singh, A.K., Muzzle point pattern based techniques for individual cattle identification. IET Image Process. 11 (2017 October), 805–814.
13. Tharwat, A., Gaber, T., Hassanien, A.E., Cattle identification based on Muzzle images using Gabor features and SVM classifier. Advanced Machine Learning Technologies and Applications, Communications in Computer and Information Science, Springer, Cham, November, 2014, 236–247.
14. Allen, A., Golden, B., Taylor, M., Patterson, D., Henriksen, D., Skuce, R., Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland. Livest. Sci. 116:July (1) (2008), 42–52.
15. Gonzales Barron, U., Corkery, G., Barry, B., Butler, F., McDonnell, K., Ward, S., Assessment of retinal recognition technology as a biometric method for sheep identification. Comput. Electron. Agric. 60:March (2) (2008), 156–166.
16. Kumar, S., Singh, S.K., Monitoring of pet animal in smart cities using animal biometrics. Future Gener. Comput. Syst.(December), 2016.
17. Wada, N., Shinya, M., Shiraishi, M., Pig face recognition using eigenspace method. ITE Trans. Media Technol. Appl. 1:4 (2013), 328–332.
18. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13:4 (2004), 600–612.
19. Zhao, W., Chellappa, R., Face Processing: Advanced Modeling and Methods. 2006, Academic Press.
20. Fisher, R.A., The use of multiple measures in taxonomic problems. Ann. Eugen. 7 (1936), 179–188.
21. Fukushima, K., Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36:4 (1980), 193–202.
22. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86:11 (1998), 2278–2324.
23. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 1–9.
24. He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 770–778.
25. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D., Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8:1 (1997), 98–113.
26. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E., Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments., 2007, University of Massachusetts, Amherst, 1 Technical Report 07, 49:1.
27. Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D., Grad-CAM: Why Did You Say That?. 2016 arXiv preprint arXiv:1611.07450.
28. Telkänranta, H., Marchant-Forde, J.N., Valros, A., Tear staining in pigs: a potential tool for welfare assessment on commercial farms. Animal 10:February (2) (2016), 318–325.