Plastic Surgery Face Recognition Using LBP and PCA Algorithm

Dadure, Pankaj; Sikder, Sayan; Sambyo, Koj

doi:10.1109/iementech.2018.8465379

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Various researches has been done on face recognition problems that addresses the above mentioned challenges related to face recognition, such as variation in expression [9], posing [22], aging [9], illumination problem [2,3,4], and disguise [10]. In addition to the mentioned challenges, another issue is of plastic surgery [25,26] that influences the performance of face recognition. Now-a-days, demand of plastic surgery has recently made plastic surgery a challenge in recognition of faces because it changes the entire facial features of the face.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition using Probabilistic Model for Locally Changed Face

Khedgaonkar¹,

Singh²,

Raghuwanshi³

et al. 2019

IJITEE

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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.

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