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

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

Granular Approach for Recognizing Surgically Altered Face Images Using Keypoint Descriptors and Artificial Neural Network

This chapter presents a new technique called entropy volume-based scale-invariant feature transform for correct face recognition post cosmetic surgery. The comparable features taken are the key points and volume of the Difference of Gaussian (DOG) structure for those points the information rate is confirmed. The information extracted has a minimum effect on uncertain changes in the face since the entropy is the higher-order statistical feature. Then the extracted corresponding entropy volume-based scale-invariant feature transform features are applied and provided to the support vector machine for classification. The normal scale-invariant feature transform feature extracts the key points based on dissimilarity which is also known as the contrast of the image, and the volume-based scale-invariant feature transform (V-SIFT) feature extracts the key points based on the volume of the structure. However, the EV-SIFT method provides both the contrast and volume information. Thus, EV-SIFT provides better performance when compared with principal component analysis (PCA), normal scale-invariant feature transform (SIFT), and V-SIFT-based feature extraction. Since it is well known that the artificial neural network (ANN) with Levenberg-Marquardt (LM) is a powerful computation tool for accurate classification, it is further used in this technique for better classification results

EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Face Recognition

Automatic recognition of people faces many challenging problems which has experienced much attention due to many applications in different fields during recent years. Face recognition is one of those challenging problem which does not have much technique to solve all situations like pose, expression, and illumination changes, and/or ageing. Facial expression due to plastic surgery is one of the additional challenges which arise recently. This paper presents a new technique for accurate face recognition after the plastic surgery. This technique uses Entropy based SIFT (EV-SIFT) features for the recognition purpose. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. But the EV- SIFT method provides the contrast and volume information. This technique provides better performance when compare with PCA, normal SIFT and V-SIFT based feature extraction

New face recognition descriptor based on edge information for surgically-altered faces in uncontrolled environment

Since plastic surgery have increasingly become common in today’s society, existing face recognition systems have to deal with its effect on the features that characterizes a person’s facial identity. Its consequences on face recognition task are that the face images of an individual can turn out to be distinct and may tend towards resembling a different individual. Current research efforts mostly employ the intensity or texture based descriptors. However, with changes in skin-texture as a result of plastic surgery, the intensity or texture based descriptors may prove deficient since they enhance the texture differences between the pre-surgery and post-surgery images of the same individual. In this thesis, the effect of plastic surgery on facial features is modelled using affine operators. On the basis of the near-shape preserving property of the combination of the operators, the following assumption is made: The edge information is minimally influenced by plastic surgery. In order to exploit this information in real-world scenarios, it requires that face images be evenly illuminated. However, an evenly illuminated face image is far from reality on applying existing illumination normalization techniques. Thus, a new illumination normalization technique termed the rgb-Gamma Encoding (rgbGE) is proposed in this thesis. The rgbGE uses a fusion process to combine colour normalization and gamma correction, which are independently adapted to the face image from a new perspective. Subsequently, a new descriptor, namely the Local Edge Gradient Gabor Magnitude (LEGGM), is proposed. The LEGGM descriptor exploits the edge information to obtain intrinsic structural patterns of the face, which are ordinarily hidden in the original face pattern. These patterns are further embedded in the face pattern to obtain the complete face structural information. Then, Gabor encoding process is performed in order to accentuate the discriminative information of the complete face structural pattern. The resulting information is then learned using subspace learning models for effective representation of faces. Extensive experimental analysis of the designed face recognition method in terms of robustness and efficiency is presented with the aid of publicly available plastic surgery data set and other data sets of different cases of facial variation. The recognition performances of the designed face recognition method on the data sets show competitive and superior results over contemporary methods. Using a heterogeneous data set that typifies a real-world scenario, robustness against many cases of face variation is also shown with recognition performances above 90%

Call Me Caitlyn: Making and making over the 'authentic' transgender body in Anglo-American popular culture

A conception of transgender identity as an ‘authentic’ gendered core ‘trapped’ within a mismatched corporeality, and made tangible through corporeal transformations, has attained unprecedented legibility in contemporary Anglo-American media. Whilst pop-cultural articulations of this discourse have received some scholarly attention, the question of why this 'wrong body' paradigm has solidified as the normative explanation for gender transition within the popular media remains underexplored. This paper argues that this discourse has attained cultural pre-eminence through its convergence with a broader media and commercial zeitgeist, in which corporeal alteration and maintenance are perceived as means of accessing one’s ‘authentic’ self. I analyse the media representations of two transgender celebrities: Caitlyn Jenner and Nadia Almada, alongside the reality TV show TRANSform Me, exploring how these women’s gender transitions have been discursively aligned with a cultural imperative for all women, cisgender or trans, to display their authentic femininity through bodily work. This demonstrates how established tropes of authenticity-via-bodily transformation, have enabled transgender to become culturally legible through the wrong body trope. Problematically, I argue, this process has worked to demarcate ideals of ‘acceptable’ transgender subjectivity: self-sufficient, normatively feminine, and eager to embrace the possibilities for happiness and social integration provided by the commercial domain

ORLAN Revisited: Disembodied Virtual Hybrid Beauty

I argued in 2000 that the French artist ORLAN may have moved away from her Reincarnation performances toward her Self-Hybridizations because she thought that in the latter she would be more transparently obvious in meaning and less frequently misunderstood. I may have overstated the ability of audiences to comprehend, however. In this essay I argue that the virtual beauty that ORLAN unfolds in her ongoing series Self-Hybridizations is not a real or actual beauty but rather a fake beauty, causally disembodied, based on the effects she intends to create from an imaginative use of combined hybrid imagery. Subverting the familiar philosophical notions of aesthetic distance and aesthetic appreciation, hers is not a monstrous beauty (as some feminist art theorists contend) but rather a fake beauty that still has aesthetic features worth assessing. I suggest the possibility of generational differences in understandings of the term 'feminist', i.e., shifts in meaning from early feminist theory of the 1970s to ever-evolving, twenty-first century notions of the term, all of which add to the confusion. As I negotiate this terrain, I hope to steer both critics and viewers more directly to the words of the artist herself, "I have tried to make my Self-Hybridations as 'human' as possible, like mutant beings, but I still did not think that the confusion could be possible.

Numerical modeling of Hemodynamics in the thoracic aorta and alterations by Dacron patch treatment of Aortic Coarctation

Coarctation of the aorta (CoA) is a major congenital heart disease, characterized by a severe stenosis of the proximal descending thoracic aorta. Traditionally, surgery has been the treatment of choice for CoA. Dacron patch aortoplasty gained increased popularity after its introduction in the mid-twentieth century due to its advantages over other surgical treatment methods available at the time. A major complication with Dacron patch aortoplasty has been the formation of late aneurysm with as much as 51% incidence reported in follow up studies. The change in aortic morphology and formation of aneurysms after Dacron patch surgery could lead to local adverse changes in hemodynamic conditions which have been correlated to long term morbidity. No study to date has investigated the local hemodynamics in the human thoracic aorta and the alterations occurring in thoracic aorta of Dacron patients in detail. Computational fluid dynamics (CFD) can be used to elucidate local hemodynamics in the thoracic aorta of Normal subjects and surgically treated CoA patients. We tested the hypothesis that Dacron patch aortoplasty causes alterations in vessel wall geometry and hemodynamic indices in the thoracic aorta of CoA patients. Patient specific CFD models were constructed for six Normal, and six age and gender matched Dacron patients. CFD simulations were performed with physiologic boundary conditions to quantify hemodynamic indices. Localized quantification of simulation results for time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) was conducted to obtain axial and circumferential plots at various spatial locations in the thoracic aorta. Velocity streamlines and vectors quantified from simulation results for Normal subjects were similar to the flow patterns demonstrated previously using medical imaging techniques. Spatial representations of instantaneous and time-averaged WSS as well as OSI were reflective of these velocity results. Alterations in patterns of velocity streamlines, vectors, TAWSS and OSI were observed for Dacron patients with respect to Normal subjects. Altered axial and circumferential patterns of TAWSS and OSI were also demonstrated for Dacron patients by localized quantification. These results may ultimately facilitate greater understanding if sites of long-term morbidity in Dacron patients correspond with these hemodynamic alterations during follow-up

THE SOUND OF SCOTOMA: A multisensory integration approach for individuals with Macular Degeneration

openAudio-spatial representation reorganizes in the absence of visual inputs, as in the case of blind individuals. However, it is not clear how this spatial reorganization works. Although blindness is an ideal condition to understand how other sensory modalities react in absence of vision, there are some limits in using it as a modal. The main limit is that blindness can be considered a stable model of cortical organization and it does not allow to understand the mechanisms which cause this reorganization. To understand this process, we have studied a unique group of individuals suffering from Macular Degeneration (MD) for whom loss of visual inputs due to a progressive scotoma is an ongoing process. In this dissertation I decided to focus on understanding auditory spatial representation in MD individuals and to develop technnological solutions for them incorporating multisensory integration. First, we developed a device called ARENA which is an audio-tactile matrix of speakers to study audio-spatial localization in MD individuals. Our findings show that visual loss brings an immediate change in the processing of audio-spatial percept by attracting the lateral sounds towards scotoma positions in the center, producing a strong auditory spatial perception bias. To recaliberate this audio-spatial bias and to give MD individuals an understanding of their own scotoma to develop an effective pseudo fovea, we have designed a rehabilitation protocol called Intelligent Audio Visual Thumble Training (IVATT). A multisensory feedback device Audio Visual Thumble (AVT) is developed for this training. Our findings show that this technique is effective to overcome the audio-spatial bias and can improve the precision towards visual stimuli in peripheral visual field. This work concludes that development of scotoma alters the audio-spatial representation and hence focus of rehabilitation techniques can be extended to bring-in multisensory modalities in order to utilize residual vision of MD individuals.embargoed_20210317XXXII CICLO - BIOINGEGNERIA E ROBOTICA - BIOENGINEERING AND ROBOTICS - Cognitive robotics, interaction and rehabilitation technologies09/C2 - FISICA TECNICA E INGEGNERIA NUCLEAREAhmad, Hafsa

Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

The use of the ocular region as a biometric cue has gained considerable traction due to recent advances in automated iris recognition. However, a multitude of factors can negatively impact ocular recognition performance under unconstrained conditions (e.g., non-uniform illumination, occlusions, motion blur, image resolution, etc.). This dissertation develops techniques to perform iris and ocular recognition under challenging conditions. The first contribution is an image-level fusion scheme to improve iris recognition performance in low-resolution videos. Information fusion is facilitated by the use of Principal Components Transform (PCT), thereby requiring modest computational efforts. The proposed approach provides improved recognition accuracy when low-resolution iris images are compared against high-resolution iris images. The second contribution is a study demonstrating the effectiveness of the ocular region in improving face recognition under plastic surgery. A score-level fusion approach that combines information from the face and ocular regions is proposed. The proposed approach, unlike other previous methods in this application, is not learning-based, and has modest computational requirements while resulting in better recognition performance. The third contribution is a study on matching ocular regions extracted from RGB face images against that of near-infrared iris images. Face and iris images are typically acquired using sensors operating in visible and near-infrared wavelengths of light, respectively. To this end, a sparse representation approach which generates a joint dictionary from corresponding pairs of face and iris images is designed. The proposed joint dictionary approach is observed to outperform classical ocular recognition techniques. In summary, the techniques presented in this dissertation can be used to improve iris and ocular recognition in practical, unconstrained environments