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Plastic Surgery Facial Image Correspondence: A Near Set Approach

by K. S. Patnaik, A. Sanober
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 1
Year of Publication: 2012
Authors: K. S. Patnaik, A. Sanober
10.5120/5656-7546

K. S. Patnaik, A. Sanober . Plastic Surgery Facial Image Correspondence: A Near Set Approach. International Journal of Computer Applications. 42, 1 ( March 2012), 16-22. DOI=10.5120/5656-7546

@article{ 10.5120/5656-7546,
author = { K. S. Patnaik, A. Sanober },
title = { Plastic Surgery Facial Image Correspondence: A Near Set Approach },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 1 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number1/5656-7546/ },
doi = { 10.5120/5656-7546 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:22.280011+05:30
%A K. S. Patnaik
%A A. Sanober
%T Plastic Surgery Facial Image Correspondence: A Near Set Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 1
%P 16-22
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article introduces perceptual resemblance of plastic surgery facial images using near sets. Near sets are disjoint sets that resemble each other. Near sets facilitate measurement of similarity between objects (digital images) based on features values (obtained by probe functions) that describe the objects. Resemblance between disjoint sets occurs whenever there are observable similarities between the objects in the sets. Each list of feature values defines an object's description. Objects that are perceived as similar based on their descriptions are grouped together. These groups of similar objects can provide information and reveal patterns about objects of interest in the disjoint sets. The practical application of near set theory on the pre and post plastic surgery facial images to extract resemblance between them was introduced in this article. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a great extent thereby posing a great challenge for face recognition algorithms. The main aim of this article is to measure the degree of resemblance of facial images before and after plastic surgery. Blepharoplasty (Eyelid surgery) and Rhinoplasty (Nose surgery) is being considered for this research work due to the maximum number of individuals and easy to differentiate faces before and after plastic surgery . tHD ,tNM and tHM is being used to measure the degree of resemblances between plastic surgery images. tHD measure shows around 100% nearness as compared to tNM and tHM for all features . These measures can also be used in increasing the efficiency of any face recognition system containing plastic surgery images.

References
  1. R. Singh, M. Vatsa, A. Ross, and A. Noore, "A mosaicing scheme for pose-invariant face recognition", IEEE Transactions on Systems, Man and Cybernetics - Part B, vol. 37, no. 5, pp. 1212–1225, 2007.
  2. R. Singh, M. Vatsa, and A. Noore, "Face recognition with disguise and single gallery images", Image and Vision Computing, vol. 27, no. 3, pp. 245–257, 2009
  3. Richa Singh, Mayank Vasta, Afzel Noore,Himanshu S. Bhatt, Samarth Bharadwaj and Shahin S. Nooreyezdan,"Plastic Surgery:A New Dimension to Face Recognition", IEEE,2008.
  4. Richa Singh, Mayank Vasta, Afzel Noore, "Effect of Plastic Surgery on Face Recognition:A Preliminary Study ",IEEE,2009.
  5. Christopher Henry and James F. Peters, "Perception based image classification", International Journal of intelligent Computing and Cybernetics", vol. 3. , 2010, no. 3, pp. 410-430.
  6. James F. Peters, "Metric spaces for Near sets", Applied Mathematical Sciences, Vol. 5, 2011, no. 2, 73-78.
  7. Christopher J. Henry, "Neighbourhoods, Classes and Near Sets", Applied Mathematical Sciences,vol. 5,2011,no. 35,1727-1732.
  8. Sheela Ramanna,"Near Tolerance Rough sets" ,Applied Mathematical Sciences,Vol. 5,2011, no. 38,1895-1902.
  9. American society for Aesthetic Plastic Surgery,"2008 statistics",http://ww. surgery. org/download/2008stats. pdf,2009
  10. Peters, J. F. : Near sets. General theory about nearness of objects. Applied Mathematical Sciences 1(53), 2609–2029 (2007), http://wren. ece. umanitoba. ca/
  11. Peters, J. F. : Near Sets. Special Theory about Nearness of Objects. Fundamenta Informaticae 76, 1–27 (2006).
  12. James F. Peters ,Piotr Wasilewski,"Foundations of near sets",Information Sciences,179,2009,pp. 3091-3109
  13. Peters, J. F. : Classification of Perceptual Objects by Means of Features. Int. J. of Information Technology and Intelligent Computing 3(2), 1–35 (2007).
  14. Pawlak, Z. : Rough Sets, Institute for Computer Science, Polish Academy of Sciences,Report 431 (March 1981).
  15. Pawlak, Z. : Classification of Objects by Means of Attributes, Institute for Computer Science, Polish Academy of Sciences, Report 429 (March 1981).
  16. Orlowska, E. : Applications of Rough Sets, Semantics of Vague Concepts, Institute for Computer Science, Polish Academy of Sciences, Report 469 (March 1982); Dorn, G. , Weingartner, P. : Foundations of Logic and Linguistics. Problems and Solutions, March 1982, pp. 465–482. Plenum Press, London (1985).
  17. J. F. Peters, "Tolerance near sets and image correspondence," International Journal of Bio-Inspired Computation, vol. 1, no. 4, pp. 239–245, 2009.
  18. C. Henry and J. F. Peters, "Perception based image classification," Computational Intelligence Laboratory, University of Manitoba, Tech. Rep. , 2009, uM CI Laboratory Technical Report No. TR-2009-016.
  19. W. Rucklidge, Efficient Visual Recognition Using Hausdorff Distance. Springer-Verlag,1996.
Index Terms

Computer Science
Information Sciences

Keywords

Near Sets Rough Sets Resemblance Plastic Surgery Tolerance Near Sets

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