Plastic Surgery: A New Dimension to Face Recognition

Singh, Richa; Vatsa, Mayank; Bhatt, Himanshu; Bharadwaj, Samarth; Noore, Afzel; Nooreyezdan, Shahin

doi:10.1109/tifs.2010.2054083

Cited by 162 publications

(148 citation statements)

References 16 publications

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“…Secondly, the same techniques are as well visually assessed, but from a multiple view-point. This second experimental analysis uses sample images from Georgia Tech Face data set (GT) [55], labelled faces in the wild data set (LFW) [56], the plastic surgery data set (PS) [57], and Essex data set [58]. These are followed by experiments run on the plastic surgery database in order to evaluate the performances of various illumination compensation (normalization) approaches in the presence of appearance variation due to plastic surgery.…”

Section: Resultsmentioning

confidence: 99%

Minimizing Separability: A Comparative Analysis of Illumination Compensation Techniques in Face Recognition

Olisah¹

2017

IJITCS

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Abstract-Feature extraction task are primarily about making sense of the discriminative features/patterns of facial information and extracting them. However, most real world face images are almost always intertwined with imaging modality problems of which illumination is a strong factor. The compensation of the illumination factor using various illumination compensation techniques has been of interest in literatures with few emphasis on the adverse effect of the techniques to the task of extracting the actual discriminative features of a sample image for recognition. In this paper, comparative analyses of illumination compensation techniques for extraction of meaningful features for recognition using a single feature extraction method is presented. More also, enhancing red, green, blue gamma encoding (rgbGE) in the log domain so as to address the separability problem within a person class that most techniques incur is proposed. From experiments using plastic surgery sample faces, it is evident that the effect illumination compensation techniques have on face images after pre-processing is highly significant to recognition accuracy.

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Section: Resultsmentioning

confidence: 99%

Minimizing Separability: A Comparative Analysis of Illumination Compensation Techniques in Face Recognition

Olisah¹

2017

IJITCS

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“…Face recognition performance in plastic surgery scenarios for cases such as rhytidectomy (face and mid-face lift), rhinoplasty (nose reshaping), blepharoplasty (eye surgery), otoplasty (ear surgery), browlift, dermabrasion, and skin peeling has been investigated [2,[22][23][24][35][36][37][38]. Bhatt et al [24,25] adopted non-disjoint face granulation approach where the granules are obtained from HE-normalized images.…”

Section: Related Workmentioning

confidence: 99%

“…Typically, unconstrained faces include faces that are subject to factors such as changes in illumination direction, pose, expression, and recently introduced variations due to plastic surgery [2]. The problem of pose, expressions, and illumination in face recognition has been addressed in a good number of literatures, some of which are [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, rhytidectomy is combined with some surgery procedures such as nose reshaping, eye lift, and jaw enhancement, which change the face appearance (increases intra-subject variation), but might not necessarily change the shape of the facial components. In other words, rhytidectomy is an embodiment of local and global appearance-changing surgery procedures, which may explain the challenges that the existing intensity-based recognition methods in the case of rhytidectomy [2] and subsequent works [22][23][24][25] faced. In contrast to the original gray-level image, the image edge information is an alternative because only valuable representation of the most significant details of the face is retained, which we presume is a good candidate for the recognition of surgically altered faces.…”

Section: Introductionmentioning

confidence: 99%

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Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

Chude-Olisah

Sulong

Chude-Okonkwo

et al. 2014

EURASIP J. Adv. Signal Process.

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Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Plastic Surgery Facial Image Correspondence: A Near Set Approach

Patnaik¹,

Sanober²

2012

IJCA

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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.

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Plastic Surgery: A New Dimension to Face Recognition

Cited by 162 publications

References 16 publications

Minimizing Separability: A Comparative Analysis of Illumination Compensation Techniques in Face Recognition

Minimizing Separability: A Comparative Analysis of Illumination Compensation Techniques in Face Recognition

Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

Plastic Surgery Facial Image Correspondence: A Near Set Approach

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