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Reseach Article

A Novel Eigenface based Species Recognition System

by T.a.s. Achala Perera, John Collins
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 20
Year of Publication: 2015
Authors: T.a.s. Achala Perera, John Collins
10.5120/20268-2675

T.a.s. Achala Perera, John Collins . A Novel Eigenface based Species Recognition System. International Journal of Computer Applications. 115, 20 ( April 2015), 19-23. DOI=10.5120/20268-2675

@article{ 10.5120/20268-2675,
author = { T.a.s. Achala Perera, John Collins },
title = { A Novel Eigenface based Species Recognition System },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number20/20268-2675/ },
doi = { 10.5120/20268-2675 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:24.310071+05:30
%A T.a.s. Achala Perera
%A John Collins
%T A Novel Eigenface based Species Recognition System
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 20
%P 19-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To develop a species recognition system for a resettable trap using novel species identification techniques. Classical Eigenface based identification techniques are widely used in human faced detection domain. In this research Eigen faced based technique is used to identify the feral animals like Possums, cat and Weasels. When traditional Eigen faced technique is applied to detect these animal, detection rate is extremely poor (Possums 55%, Cats 33% and weasels 45%), due to their orientation of the heads and fur patterns. In this research, Eigenface based image recognition technique's detection rate was improved by adding different training sets to the system. Traditional Eigenface detection domain one training set is used, but it was discovered single training set was not adequate to detect small animal. This is because smaller animals like possums, cats and weasels tend to have different color group, different texture and hard to obtain face up images. Therefore it was decided to divide the training set into different sub groups. This sub training sets are used to train system and search for match. This method improved the detection rate up to 83% for possum, 50% for cats and 63% weasels.

References
  1. D. W. Craig Gillies, "A short guide for identifying footprints on tracking tunnel papers," vol. OLDDM-63018 1
  2. Z. Weiwei, S. Jian, and T. Xiaoou, "From Tiger to Panda: Animal Head Detection," Image Processing, IEEE Transactions on, vol. 20, pp. 1696-1708, 2011.
  3. N. H. JAMES C. RUSSELL, REINHARD KLETTE, AND BODO ROSENHAHN, "Automatic track recognition of footprints for identifying cryptic species," the Ecological Society of America, vol. 90, pp. 2007 - 2013, 2009.
  4. N. H. Guannan Yuan, James Russell, Reinhard Klette, and Bodo Rosenhahn, "Understanding Tracks of Different Species of Rats," The University of Auckland, Auckland 187, 2006.
  5. R. G. -G. Alexandre R. T. Palma, "Morphometric identification of small mammal footprints from ink tracking tunnels in the Brazilian Cerrado," Revista Brasileira de Zoologia, vol. 24, pp. 333 - 343, 2007.
  6. L. T. Antelo, T. Ordonez, I. Minino, J. Gracia, E. Ribes, J. Hervas, S. Simon, and A. A. Alonso, "A vision-based system for on-board identification and estimation of discarded bio-mass: A tool for contributing to marine resources sustainability," in OCEANS, 2011 IEEE - Spain, pp. 1-8.
  7. Ramanan D. , Forsyth D. A. , and Barnard K. , "Building models of animals from video," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, pp. 1319-1334, 2006.
  8. K. J. Dana, S. K. Nayar, B. van Ginneken, and J. J. Koenderink, "Reflectance and texture of real-world surfaces," in Computer Vision and Pattern Recognition, 1997. Proceedings. , 1997 IEEE Computer Society Conference on, 1997, pp. 151-157.
  9. T. Burghardt and J. Calic, "Real-time Face Detection and Tracking of Animals," in Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on, 2006, pp. 27-32.
  10. T. Burghardt and J. Calic, "Analysing animal behaviour in wildlife videos using face detection and tracking," Vision, Image and Signal Processing, IEE Proceedings -, vol. 153, pp. 305-312, 2006.
  11. P. Viola and M. J. Jones, "Robust Real-Time Face Detection," Int. J. Comput. Vision, vol. 57, pp. 137-154, 2004.
  12. R. Lienhart and J. Maydt, "An extended set of Haar-like features for rapid object detection," in Image Processing. 2002. Proceedings. 2002 International Conference on, 2002, pp. I-900-I-903 vol. 1.
  13. P. Viola and M. Jones, "Robust real-time face detection," in Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 2001, pp. 747-747.
  14. M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Computer Vision and Pattern Recognition, 1991. Proceedings CVPR '91. , IEEE Computer Society Conference on, 1991, pp. 586-591.
  15. M. Smiatacz, "Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces – How to Face the Face Verification Task," in Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. vol. 226, R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, and A. Zolnierek, Eds. , ed: Springer International Publishing, 2013, pp. 187-196.
  16. M. Turk and A. Pentland, "Eigenfaces for Recognition," Cognitive Neuroscience, Journal of, vol. 3, pp. 71-86, 1991.
  17. P. N. Belhumeur, J. P. Hespanha, and D. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, pp. 711-720, 1997.
Index Terms

Computer Science
Information Sciences

Keywords

Euclidean distance face recognition animal detection