Plastic Surgery Facial Image Correspondence: A Near Set Approach

Abstract

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. rejection of genuine users or acceptance of impostors. To this challenge yet much literature is not available. Very few researchers till now have contributed in this field. In (3) authors have shown the comparative study of different face recognition algorithms for plastic surgery. Based on the experimentation carried out by authors it has been concluded that face recognition algorithms such as PCA, FDA, GF, LLA, LBP and GNN have shown recognition rate not more than 40% for local plastic surgery. Moreover, for global surgery it was merely up to 10%. Among all the algorithms, geometrical feature based approach has proven to a great extent comparatively for local plastic surgery. This article introduces perceptual resemblance of plastic surgery facial images using near sets . Near sets were introduced by James Peters in 2006(10) and formally defined in 2007(11) and elaborated in (12). Near sets result from a generalization of rough set theory. One set X is near another set Y to the extent that the description of at least one of the objects in X matches the description of at least one of the objects in Y. The hallmark of near set theory is object description and the classification of objects by means of features (13). Rough sets were introduced by Zdzislaw Pawlak during the early 1980s (14)(15) and provide a basis for perception of objects viewed on the level of classes rather than the level of individual objects. A fundamental basis for near set as well as rough set theory is the approximation of one set by another set considered in the context of approximation spaces. It was observed by Ewa Orlowska in 1982 that approximation spaces serve as a formal counterpart of perception, or observation