Implementation of perceptual resemblance of local plastic surgery facial images using Near Sets

Author(s):  
Prachi V. Wagde ◽  
Roshni Khedgaonkar
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Alan T. Makhoul ◽  
Kianna R. Jackson ◽  
Brian C. Drolet ◽  
Galen Perdikis

Author(s):  
K R Singh ◽  
M M Raghuwanshi ◽  
M A Zaveri ◽  
James F. Peters

Computer vision is a process of electronically perceiving and understanding of an image like human vision system (HVS) do. Face recognition techniques (FRT) determines the identity of the individual by matching the facial images with the one stored in the facial database. The performance of FRT is greatly affected by variations in face due to different factors. It is interesting to study how well these issues are being handled by RST and near set theory to improve the performance. The variation in illumination and plastic surgery changes the appearance of face that introduces imprecision and vagueness. One part of chapter introduces the adaptive illumination normalization technique using RST that classifies the image illumination into three classes based on which illumination normalization is performed using an appropriate filter. Later part of this chapter introduces use of near set theory for FRT on facial images that have previously undergone some feature modifications through plastic surgery.


Face recognition is an attention-grabbing area in research field due to various challenges like aging, pose variation, facial expression, and illumination problem. Now-a-days, plastic surgery is a standout amongst the above mentioned exciting issues of face recognition. Local plastic surgery is a type of plastic surgery in which any one feature of the face is changed instead of all features of face. In this paper, the face recognition on local plastic surgical faces using probabilistic approach is presented, where a probabilistic approach like Naive Bayes Classifier, Neural Network Classifier are used to recognize the faces with local plastic surgery from the database. Naive Bayes classifier is fused with Expectation Maximization Algorithm (EMA) for better recognition of the faces from the database. Finally, Results of Naive Bayes Classifier, Naive Bayes Classifier with EMA is evaluated on standard Plastic Surgery Database(PSD). Similarly, Neural network classifier is also been tested on PSD database, which will aid to decide which classifier is efficient for recognizing plastic surgical faces. The motive of this paper is give increase in recognition rate with the help of effective classifier.


1995 ◽  
Vol 22 (4) ◽  
pp. 791-796
Author(s):  
Paul S. Howard ◽  
Paul M. Gardner ◽  
Luis O. Vasconez ◽  
Grady B. Core
Keyword(s):  

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