A sparse representation approach to face matching across plastic surgery

Aggarwal, Gaurav; Biswas, Soma; Flynn, Patrick J.; Bowyer, Kevin W.

doi:10.1109/wacv.2012.6163008

Cited by 45 publications

(58 citation statements)

References 17 publications

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“…Other alterations include aging (a natural biological change) and plastic surgery (a medically induced change). Recent work has focused on the impact of plastic surgery on face recognition [1] [14][13] [3]. However, such surgical alterations are generally costly and permanent.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the aforementioned effects, cosmetics can also be used to successfully camouflage as well as affect wrinkles, birth moles, scars and tattoos. A vast cosmetics market 1 -typically targeted towards women -attempts to improve facial aesthetics while projecting good health. The beautification effects induced by cosmetics have been studied in recent research literature [6] [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Can facial cosmetics affect the matching accuracy of face recognition systems?

Dantcheva

Chen

Ross

2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

155

168

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The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying makeup. We present experimental results on both databases that reveal the effect of makeup on automated face recognition and suggest that this simple alteration can indeed compromise the accuracy of a biometric system. While these are early results, our findings clearly indicate the need for a better understanding of this face altering scheme and the importance of designing algorithms that can successfully overcome the obstacle imposed by the application of facial makeup.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can facial cosmetics affect the matching accuracy of face recognition systems?

Dantcheva

Chen

Ross

2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

155

168

View full text Add to dashboard Cite

show abstract

“…al., in [6], have proposed a new multimodal bio metric using face and periodical bio-metric for the recognition of face invariant to plastic surgery. This method [7], has proposed a novel approach to address the challenges involved in automatic matching of faces across plastic surgery variations. They proposed a part-wise sparse representation Approach combined with the popular sparse representation to address the challenge of plastic surgery variations and utilizes images from sequestered non-gallery subjects with similar local facial characteristics to fulfill this requirement.…”

Section: Related Workmentioning

confidence: 99%

Pattern Recognition of Surgically Altered Face Images Using MultiObjective Evolutionary Algorithm

Patil¹

2015

IJRITCC

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Abstract-Plastic surgery has been recently coming up with a new and important aspect of face recognition alongside pose, expression, illumination, aging and disguise. Plastic surgery procedures changes the texture, appearance and the shape of different facial regions. Therefore, it is difficult for conventional face recognition algorithms to match a post-surgery face image with a pre-surgery face image. The non-linear variations produced by plastic surgery procedures are hard to be addressed using current face recognition algorithms. The multi-objective evolutionary algorithm is a novel approach for pattern recognition of surgically altered face images. The algorithms starts with generating nondisjoint face granules and two feature extractors EUCLBP (Extended Uniform Circular Local Binary Pattern) and SIFT (Scale Invariant Feature Transform), are used to extract discriminating facial information from face granules.

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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%

“…In [23], a Gabor patch classifier that uses the rank-order list fused from equal and non-overlapping patches of surgically altered face images for discrimination was proposed. Aggarwal et al [36] adopted a part-wise sparse representation approach for matching plastic surgery-altered faces. They employed the intensity characteristics of the principal component analysis (PCA)-based representation of the six facial components cropped from each face image.…”

Section: Related Workmentioning

confidence: 99%

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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A sparse representation approach to face matching across plastic surgery

Cited by 45 publications

References 17 publications

Can facial cosmetics affect the matching accuracy of face recognition systems?

Can facial cosmetics affect the matching accuracy of face recognition systems?

Pattern Recognition of Surgically Altered Face Images Using MultiObjective Evolutionary Algorithm

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

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