貓(家貓)於我們的社會扮演重要的角色，牠們對於飼主而言就像是伴侶、家人甚至是小孩，陪伴並給人心靈上的滿足。遺失、調換、遭竊等狀況是全球性的問題，因此對於擁有貓的人來說可靠的辨識是很有效的管理工具。傳統的辨識方法還不夠完備，不夠保險，且這些方法可能會對於貓有不利的影響。儘管以動物外表來識別的方式在近年來備受關注，然而這些方式仍不足以用來辨識貓。

因此在本篇論文我們提出了一個新的生物識別方法，以貓的鼻子來識別貓。我們蒐集了70隻貓，共700張貓鼻子影像來建立貓資料庫，基於這個貓資料庫，我們建立了有代表性且具資料局部性限制的字典來辨識貓。與一些基於特徵的演算法相比，實驗結果證明我們的方法來辨識貓較為有效。

Cat (Felis catus) plays an important social role within our society and can provide considerable emotional support for their owners. Missing, swapping, theft, and false insurance claims of cat have become global problem throughout the world. Reliable cat identification is thus an essential factor in the effective management of the owned cat population. The traditional cat identification methods by permanent (e.g., tattoos, microchip, ear tips/notches, and freeze branding), semi-permanent (e.g., identification collars and ear tags), or temporary (e.g., paint/dye and radio transmitters) procedures are not robust to provide adequate level of security. Moreover, these methods might have adverse effects on the cats. Though the work on animal identification based on their phenotype appearance (face and coat patterns) has received much attention in recent years, however none of them specifically targets cat.

In this paper, we therefore propose a novel biometrics method to recognize cat by exploiting their noses that are believed to be a unique identifier by cat professionals. As the pioneer of this research topic, we first collect a Cat Database that contains 700 cat nose images from 70 different cats. Based on this dataset, we design a representative dictionary with data locality constraint for cat identification. Experimental results well demonstrate the effectiveness of the proposed method compared to several state-of-the-art feature-based algorithms.

[1] “World society for the protection of animals. Identification methods for
dogs and cats.” Available at http://www.icam-coalition.org/downloads/
Identification%20methods%20for%20dogs%20and%20cats.pdf.
[2] “The humane society of the united states. Pets by the numbers.” Available at
http://www.humanesociety.org/issues/pet_overpopulation/facts/
pet_ownership_statistics.html.
[3] J. Melanson, “Animal bliss. Cat Nose Prints as Unique as Human Fingerprints,
Noseprint ID.” Available at http://www.animalbliss.com/
cat-nose-prints-as-unique-as-human-fingerprints/.
[4] J. Kelley, “Catster. Four Cool Facts About Your Cat’s Nose.” Available at http:
//www.catster.com/lifestyle/cats-four-facts-nose.
[5] C.-P. Wei, Y.-W. Chao, Y.-R. Yeh, and Y.-C. F. Wang, “Locality-sensitive dictionary
learning for sparse representation based classification,” Pattern Recognition, vol. 46,
no. 5, pp. 1277–1287, 2013.
[6] M. Yang, L. Zhang, J. Yang, and D. Zhang, “Robust sparse coding for face recognition,”
in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference
on, pp. 625–632, IEEE, 2011.
[7] L. Ma, C. Wang, B. Xiao, and W. Zhou, “Sparse representation for face recognition
based on discriminative low-rank dictionary learning,” in Computer Vision and Pattern
Recognition (CVPR), 2012 IEEE Conference on, pp. 2586–2593, IEEE, 2012.
[8] F. Liu, J. Tang, Y. Song, X. Xiang, and Z. Tang, “Local structure based sparse representation
for face recognition with single sample per person,” in Image Processing
(ICIP), 2014 IEEE International Conference on, pp. 713–717, IEEE, 2014.
[9] V. L. Voith, E. Ingram, K. Mitsouras, and K. Irizarry, “Comparison of adoption
agency breed identification and dna breed identification of dogs,” Journal of Applied
Animal Welfare Science, vol. 12, no. 3, pp. 253–262, 2009.
[10] S. Kumar and S. K. Singh, “Biometric recognition for pet animal,” Journal of Software
Engineering and Applications, vol. 2014, 2014.
[11] M. Turk, A. P. Pentland, et al., “Face recognition using eigenfaces,” in Computer
Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society
Conference on, pp. 586–591, IEEE, 1991.
[12] W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, “Discriminant
analysis of principal components for face recognition,” in Face Recognition, pp. 73–
85, Springer, 1998.
[13] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent
component analysis,” Neural Networks, IEEE Transactions on, vol. 13, no. 6,
pp. 1450–1464, 2002.
[14] A. Noviyanto and A. M. Arymurthy, “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, p. 114, 2012.
[15] A. I. Awad, A. E. Hassanien, and H. M. Zawbaa, “A cattle identification approach
using live captured muzzle print images,” in Advances in Security of Information and
Communication Networks, pp. 143–152, Springer, 2013.
[16] A. Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, “Cattle
identification using muzzle print images based on texture features approach,” in
Proceedings of the Fifth International Conference on Innovations in Bio-Inspired
Computing and Applications IBICA 2014, pp. 217–227, Springer, 2014.
[17] S. Singh, A. Gupta, and A. Efros, “Unsupervised discovery of mid-level discriminative
patches,” Computer Vision–ECCV 2012, pp. 73–86, 2012.
[18] R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, “Self-taught learning: transfer
learning from unlabeled data,” in Proceedings of the 24th international conference
on Machine learning, pp. 759–766, ACM, 2007.
[19] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition
via sparse representation,” Pattern Analysis and Machine Intelligence, IEEE
Transactions on, vol. 31, no. 2, pp. 210–227, 2009.
[20] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear
coding for image classification,” in Computer Vision and Pattern Recognition
(CVPR), 2010 IEEE Conference on, pp. 3360–3367, IEEE, 2010.
[21] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM
Transactions on Intelligent Systems and Technology, vol. 2, pp. 27:1–27:27, 2011.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[22] M. A. Stricker and M. Orengo, “Similarity of color images,” in IS&T/SPIE’s Symposium
on Electronic Imaging: Science & Technology, pp. 381–392, International
Society for Optics and Photonics, 1995.
[23] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,”
in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer
Society Conference on, vol. 1, pp. 886–893, IEEE, 2005.
[24] T. S. Lee, “Image representation using 2d gabor wavelets,” Pattern Analysis and
Machine Intelligence, IEEE Transactions on, vol. 18, no. 10, pp. 959–971, 1996.