Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2018.12.3.4Keywords:
content-based image retrieval, invariant feature descriptor, multimedia databases, template matching, visual animal biometrics.Abstract
Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%.
Downloads
References
Balazs, G.H., Factors Affecting the Retention of Metal Tags on Sea Turtles, Marine Turtle Newsletter, 20, pp. 11-14, 1982.
Mrosovsky, N. & Shettleworth, S.J., What Double Tagging Studies Can Tell Us, Marine Turtle Newsletter, 22, pp. 11-15, 1982.
Limpus, C.J., Estimation of Tag Loss in Marine Turtle Research, Wildlife Research, 19, pp. 457-469, 1992.
Dam R.P. van & Diez, C.E., Differential Tag Retention in Caribbean Hawksbill Turtles, Chelonian Conservation and Biology, 3(2), pp. 225-229, 1999.
Bellini, C., Godfrey, M.H. & Sanches, T.M., Metal Tag Loss in Wild Juvenile Hawksbill Sea Turtles (Eretmochelys Imbricata), Herpetological review, 32(3), pp. 172-173, 2001.
McDonald, D.L. & Dutton, P.H., Use of PIT Tags and Photoidentification to Revise Remigration Estimates of Leatherback Turtles (Dermochelys coriacea) Nesting in St. Croix, U. S. Virgin Islands, 1979-1995, Chelonian Conservation and Biology, 2, pp. 148-152, 1996.
Bennett, P., Keuper-Bennett, U. & Balazs, G. H., Photographic Evidence for the Regression of Fibropapillomas Afflicting Green Turtles at Honokowai, Maui, in the Hawaiian Islands, in Proceedings of the Nineteenth Annual Symposium on Sea Turtle Biology and Conservation. NOAA Technical Memorandum NMFS-SEFSC-443, 2000.
Reisser, J.W., Proietti, M.C., Kinas, P.G. & Sazima, I., Photographic Identification of Sea Turtles: Method Description and Validation, with an Estimation of Tag Loss, Endangered Species Research, 5, pp. 73-82, 2008.
Wyneken, J. & Witherington, D., The Anatomy of Sea Turtles, National Marine Fisheries Service, 2001.
Boulenger, G.A., Fauna of British India, Reptilia And Batrachia, Taylor & Francis, 1890.
Burghardt, T., Barham, P.J., Campbell, N., Cuthill, I.C., Sherley, R.B. & Leshoro, T.M., A Fully Automated Computer Vision System for the Biometric Identification of African Penguins (Spheniscus Demersus) on Robben Island, in 6th International Penguin Conference (IPC07), Hobart, Tasmania, Australia, E.J. Woehler Ed., 2007.
Belongie, S., Malik, J. & Puzicha, J., Shape Context: A New Descriptor for Shape Matching and Object Recognition, in Advances In Neural Information Processing Systems, 2001.
Burghardt, T. & Campbell, N., Generic Phase Curl Localisation for an Individual Identification of Turing-Patterned Animals, Visual Observation and Analysis of Animal and Insect Behavior, pp. 17-21, 2010.
Dabarera, R. & Rodrigo, R., Vision Based Elephant Recognition for Management and Conservation, in 5th International Conference on Information and Automation for Sustainability, 2010.
Loos, A. & Pfitzer, M., Towards Automated Visual Identification of Primates Using Face Recognition, in 19th International Conference on Systems, Signals and Image Processing, 2012.
Taha, A., Darwish, A. & Hassanien, A.E., Arabian Horse Identification System Based on Live Captured Muzzle Print Images, in International Conference on Advanced Intelligent Systems and Informatics, 2017.
Lowe, D.G., Object Recognition from Local Scale-Invariant Features, in the proceedings of the 7th IEEE international Conference on Computer Vision, 1999.
Monteiro, F.C., Automatic Cattle Identification Using Graph Matching Based on Local Invariant Features, in International Conference Image Analysis and Recognition, 2016.
Li, W., Ji, Z., Wang, L., Sun, C. & Yang, X., Automatic Individual Identification of Holstein Dairy Cows Using Tailhead Images, Computers and Electronics in Agriculture, 142, pp. 622-631, 2017.
Dorai, C.N., Ratha, K. & Bolle, R.M., Detecting DYNAMIC BEHAVIOR in Compressed Fingerprint Videos: Distortion, in IEEE Conference on Computer Vision and Pattern Recognition, 2000.
Bay, H., Tuytelaars, T. & Van Gool, L., Surf: Speeded Up Robust Features, in ECCV, p.404-417, 2006.
Rublee, E., Rabaud, V., Konolige, K. & Bradski, G., ORB: An Efficient Alternative to SIFT or SURF, in IEEE International Conference on Computer Vision (ICCV), 2011.
Dalal, N. & Triggs, B., Histograms of Oriented Gradients for Human Detection, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
Ting, R.R., Nurhartini Bids Farewell to Her Turtles, 5 Dec 2016. [Online]. [Accessed 10/1/18].
Hipiny, I. & Mayol-Cuevas, W., Recognising Egocentric Activities from Gaze Regions with Multiple-Voting Bag of Words, Technical Report CSTR12-003, University of Bristol, 2012.
Hipiny, I., Egocentric Activity Recognition Using Gaze, PhD thesis, University of Bristol, 2013.
Ujir, H., Sing, L.C. & Hipiny, I., A Modular Approach and Voting Scheme on 3D Face Recognition, in International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2014.
Ujir, H., Spann, M. & Hipiny, I., 3D Facial Expression Classification using 3D Facial Surface Normals, The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications, pp. 245-253, 2014.
