Biometric Recognition for Pet Animal

Santosh Kumar, Sanjay Kumar Singh

Department of Computer Science & Engineering, I.I.T. (BHU), Varanasi, India.

DOI: 10.4236/jsea.2014.75044                

Abstract

Missing, swapping, false insurance claims and reallocation of pet animals (dog) are global problems throughout the world and research done to solve this problem is minimal. Traditional biometrics and non-biometrics methods have their own boundaries and they fail to provide competent level of security to pet animal (dog). The work on animal identification based on their phenotype appearance (coat patterns) has been an active research area in recent years and automatic face recognition for dog is not reported in the literature. Dog identification needs innovative research to protect the pet animal. Therefore it is imperative to initiate research, so that future face recognition algorithm will be able to solve this important problem for identification of pet animal (like dog, cat). In this paper an attempt has been made to minimize the above mentioned problems by biometrics face recognition of dog. The contributions of this research are: 1) implementation of an existing biometrics algorithm which mitigates the effects of covariates for dogs; 2) proposed fusion based method for recognition of pet animal with 94.86% accuracy. Thus in this paper, we have tried to demonstrate that face recognition of dog can be used to recognize the dog efficiently.

Keywords

Animal Biometrics, Pet Animal, Face Recognition, Dog Feature Covariates

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Palika, L. (2004) Purebred Rescue Dog Adaptation: Rewards and Realities. John Wiley & Sons, Hoboken.
[2]	http://www.komonews.com/news/local/New-app-uses-facial-recognition-for-dogs-to-find-lost-pets-225590872.html
[3]	Hjalmar, S.K. and Burghardt, T. (2013) Animal Biometrics: Quantifying and Detecting Phenotypic Appearance. Trends in Ecology and Evolution, 28, 432-41. http://dx.doi.org/10.1016/j.tree.2013.02.013
[4]	Petersen, W.E. (1992) The Identification of the Bovine by Means of Nose-Prints. Journal of Dairy Sciences, 5, 249-258. http://dx.doi.org/10.3168/jds.S0022-0302(22)94150-5
[5]	Viola, P. and Jones, M. (2004) Robust Real-Time Face Detection. International Journal of Computer Vision, 57, 137-154. http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb
[6]	Gonzales, B.U., Corkery, G., Barry B., Butler, F., McDonnell, K. and Ward, S. (2008) Assessment of Retinal Recognition Technology as a Biometric Method for Sheep Identification. Journal of Computers and Electronics in Agriculture, 60, 156-166. http://dx.doi.org/10.1016/j.compag.2007.07.010
[7]	Yamada, A., Kojima, K., Kiyama J., Okamoto, M. and Murata, H. (2011) Directional Edge-Based Dog and Cat Face Detection Method for Digital Camera. Proceedings of International Conference on Consumer Electronics IEEE (ICCE), Las Vegas, 9-12 January 2011, 87-88.
[8]	Ketprom, U., et al. (2011) RFID for Cattle Traceability System at Animal Checkpoint. Proceedings of the IEEE Conference on Annual Service Research &Innovative Institute (SRII), San Jose, March 29 2011-April 2 2011, 517-521.
[9]	Parkhi, O.M., Vedaldi, A., Zisserman, A. and Jawahar, C.V. (2012) Cats and Dogs. IEEE Computer Vision and Pattern Recognition, Providence, 16-21 June 2012, 3498-3505.
[10]	Chanvichitkul, M., Kumhom, P. and Chamnongthai, K. (2007) Face Recognition Based Dog Breed Classification Using Coarse-to-Fine Concept and PCA. Proceedings of Asia-Pacific Conference on Communications, Bangkok, 18-20 October 2007, 25-29.
[11]	Voithi, V.L., et al. (2013) Comparison of Visual and DNA Breed Identification of Dogs and Inter-Observer Reliability. American Journal of Sociological Research, 3, 17-29.
[12]	Voith, V.L., et al. (2009) Comparison of Adoption Agency Breed Identification and DNA Breed Identification of Dogs. Journal of Applied Animal Welfare Science, 12, 253-262. http://dx.doi.org/10.1080/10888700902956151
[13]	Dogster. http://www.dogster.com/
[14]	Flickr. http://www.flicker.com/
[15]	Google Images. http://images.google.com
[16]	Catster. http://www.catster.com/
[17]	Mydogspace. http://www.mydogspace.com/
[18]	Turk, M.A. and Pentland, A.P. (1991) Face Recognition Using Eigenfaces. Proceedings of IEEE Computer Vision and Pattern Recognition, Maui, 3-6 June 1991, 586-591.
[19]	Zhao, W., Chellappa, R. and Krishnaswamy, A. (1998) Discriminant Analysis of Principal Components for Face Recognition. Proceedings of the International Conference on Face and Gesture Recognition, Nara, 14-16 April 1998, 336-341.
[20]	Bartlett, M.S., Movellan Javier, R. and Sejnowski, T.J. (2002) Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks, 13, 1450-1464. http://dx.doi.org/10.1109/TNN.2002.804287
[21]	Ojala, T., Pietikainen, M. and Maenpaa, T. (2002) Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971-987. http://dx.doi.org/10.1109/TPAMI.2002.1017623
[22]	Ahonen, T., Hadid, A. and Pietikainen, M. (2006) Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 2037-2041. http://dx.doi.org/10.1109/TPAMI.2006.244
[23]	Belhumeur, P.N., Hespanha, J.P. and Kriegman, D. (1997) Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 711-720. http://dx.doi.org/10.1109/34.598228
[24]	Zhao, H. and Yuen, P.C. (2008). Incremental Linear Discriminant Analysis for Face Recognition. Systems, Man, and Cybernetics. Part B: IEEE Transactions on Cybernetics, 38, 210-221. http://dx.doi.org/10.1109/TSMCB.2007.908870
[25]	Weng, J., Zhang, Y. and Hwang, W.-S. (2003) Candid Covariance-Free Incremental Principal Component Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1034-1040. http://dx.doi.org/10.1109/TPAMI.2003.1217609
[26]	Chang, C.C. and Lin, C.-J. (2001) LIBSVM: A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[27]	Heisele, B., Ho, P. and Poggio, T. (2001) Face Recognition with Support Vector Machines: Global versus Component-Based Approach, Proceedings of 8th IEEE International Conference on Computer Vision (ICCV) Vancouver, 2, 688-694.
[28]	Guo, G., Li, S.Z. and Chan, K. (2001) Face Recognition by Support Vector Machines. Image and Vision Computing, 19, 631-638. http://dx.doi.org/10.1016/S0262-8856(01)00046-4