Part Of Face Research Articles

FakeLocator: Robust Localization of GAN-Based Face Manipulations

Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media forensics area, detecting and ultimately locating the image forgery has become an imperative task. In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization. Based on this basic observation, we have proposed a novel approach, termed <i>FakeLocator</i>, to obtain high localization accuracy, at full resolution, on manipulated facial images. To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map that preserves more information of fake regions. To improve the universality of <i>FakeLocator</i> across multifarious facial attributes, we introduce an attention mechanism to guide the training of the model. To improve the universality of <i>FakeLocator</i> across different DeepFake methods, we propose partial data augmentation and single sample clustering on the training images. Experimental results on popular FaceForensics&#x002B;&#x002B;, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method. Compared with the baselines, our method performs better on various metrics. Moreover, the proposed method is robust against various real-world facial image degradations such as JPEG compression, low-resolution, noise, and blur.

Dynamic Feature Subset Selection for Occluded Face Recognition

Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.

Learning Discriminant Spatial Features With Deep Graph-Based Convolutions for Occluded Face Detection

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.

Recent Generative Adversarial Approach in Face Aging and Dataset Review

Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks. In this study, we review a classic approach that uses a generative adversarial network. The structure, formulation, learning algorithm, challenges, advantages, and disadvantages of the algorithms contained in each proposed algorithm are discussed systematically. Generative Adversarial Networks are an approach that obtains the status of the art in the field of face aging by adding an aging module, paying special attention to the face part, and using an identity-preserving module to preserve identity. In this paper, we also discuss the database used for facial aging, along with its characteristics. The dataset used in the face aging process must have the following criteria: (1) a sufficiently large age group in the dataset, each age group must have a small range, (2) a balanced distribution of each age group, and (3) has enough number of face images.

Automatic identification of smoking behaviour in public places based on improved YOLO algorithm

This paper proposes an automatic identification method of smoking behaviour in public places based on the improved You Only Look Once (YOLO) algorithm. The histogram of oriented gradient (HOG) feature method is used to detect the face part of smokers, taking the detected face image as the input, the edge of the image is extracted by using the structured edge detection operator of the edge box. The gesture dataset is re-clustered by K-means++ clustering algorithm to obtain a priori frame that is more suitable for gesture recognition; and in the traditional YOLO network, the SPP spatial pyramid pooling module is added to realise the fusion of local gesture features and global gesture features, to complete the automatic identification of smoking behaviour in public places. The results show that the highest recognition rate of this method is 94.3%, the accuracy and recall rate are higher than those of the traditional methods, and the recognition results are more comprehensive.

Spatial tuning of face part representations within face-selective areas revealed by high-field fMRI.

Regions sensitive to specific object categories as well as organized spatial patterns sensitive to different features have been found across the whole ventral temporal cortex (VTC). However, it is unclear that within each object category region, how specific feature representations are organized to support object identification. Would object features, such as object parts, be represented in fine-scale spatial tuning within object category-specific regions? Here, we used high-field 7T fMRI to examine the spatial tuning to different face parts within each face-selective region. Our results show consistent spatial tuning of face parts across individuals that within right posterior fusiform face area (pFFA) and right occipital face area (OFA), the posterior portion of each region was biased to eyes, while the anterior portion was biased to mouth and chin stimuli. Our results demonstrate that within the occipital and fusiform face processing regions, there exist systematic spatial tuning to different face parts that support further computation combining them.

Experience Professional dermatomycosis of the face, neck and upper part of chest caused by T. mentagrophytes

A 43-year-old female zoologist has an occupational dermatomycosis of the face, neck and upper part of chest caused by T. mentagrophytes. It was mistakenly diagnosed and treated as "Rosacea, papulopustular subtype, ocular rosacea" for 1.5 years. Symmetrical, atypical clinical manifestations with recurrent blepharoconjunctivitis developed as a result of self-treatment using a topical steroid. The detection of cicatrizing folliculitis in the eyebrow area and an arcual peripheral edge in fresh foci on the chest helped to suspect dermatomycosis. The definitive diagnosis was made after the detection of abundant coarse septate mycelium during microscopic investigation of skin scales from foci on the face and chest with 30% KOH and an abundant growth of Trichophyton mentagrophytes during a cultural research study of skin scales, eyebrow hair and eyelashes from lesions. The use of terbinafine for 4 weeks led to the recovery of the patient.

Task Modulation of Single-Neuron Activity in the Human Amygdala and Hippocampus.

The human amygdala and hippocampus are critically involved in various processes in face perception. However, it remains unclear how task demands or evaluative contexts modulate processes underlying face perception. In this study, we employed two task instructions when participants viewed the same faces and recorded single-neuron activity from the human amygdala and hippocampus. We comprehensively analyzed task modulation for three key aspects of face processing and we found that neurons in the amygdala and hippocampus (1) encoded high-level social traits such as perceived facial trustworthiness and dominance and this response was modulated by task instructions; (2) encoded low-level facial features and demonstrated region-based feature coding, which was not modulated by task instructions; and (3) encoded fixations on salient face parts such as the eyes and mouth, which was not modulated by task instructions. Together, our results provide a comprehensive survey of task modulation of neural processes underlying face perception at the single-neuron level in the human amygdala and hippocampus.

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.

Effects of different processes of tunneling on displacements soil using 3D Finite Element Method

Abstract The excavation process of tunnels induces stresses and deformation in the surrounding soil. The method of excavation is one of the major problems related to the safety of the operators and the ground stability during the construction of underground works. So, it is necessary to choose an ideal method to minimize the displacements and stresses induced by tunneling. The main aim of this study is to simulate numerically the effect of different processes of tunneling on ground displacements, the settlements at surface soil and the internal efforts induced in the lining tunnel; in order to select the best process of excavation, which gives us a less effects on displacements generated by tunneling, thus, ensuring the stability and the solidity of the underground constructions. In addition, this study allows us to control and to predict the diverse movements generated by tunneling (displacements, settlements, efforts internes) exclusively for the shallow tunnel nearby to the underground constructions in the urban site. This modeling will be done by employing five different processes for tunnel excavation using the NATM (New Austrian Tunneling Method) method. The first process, the modeling of the excavation tunnel, is done almost in the same way as in reality; the partial face excavation, with seven slices, made by the excavation. The second process, by partial face excavation, is divided into eleven slices, next, we used the partial face excavation by nine slices, and then in thirteen slices. Finally, the dig is made by full-face excavation. The paper contributes to the prediction of the response of the soil environment to tunnel excavation using the NATM method and to minimize the diverse movements generated by tunneling. The appropriately chosen methodology confirms that displacements and subsidence are strongly influenced by the tunneling method. The three-dimensional Finite Elements Method using Plaxis3D program has been applied in the numerical simulation. The study resulted in the recommendation of a process that minimizes the effect of excavation on subsidence and ground displacement for a particular Setiha tunnel.

Contribution of Interesting Facial Parts on Gender Recognition System Performance

Contribution of Interesting Facial Parts on Gender Recognition System Performance

DE-GAN: Domain Embedded GAN for High Quality Face Image Inpainting

Domain knowledge of face shapes and structures plays an important role in face inpainting. However, general inpainting methods focus mainly on the resolution of generated images without considering the particular structure of human faces and generally produce inharmonious facial parts. Existing face-inpainting methods incorporate only one type of facial feature for face completion, and their results are still undesirable. To improve face inpainting quality, we propose a Domain Embedded Generative Adversarial Network (DE-GAN) for face inpainting. DE-GAN embeds three types of face domain knowledge (i.e., face mask, face part, and landmark image) via a hierarchical variational auto-encoder (HVAE) into a latent variable space to guide face completion. Two adversarial discriminators, a global discriminator and a patch discriminator, are used to judge whether the generated distribution is close to the real distribution or not. Experiments on two public face datasets demonstrate that our proposed method generates higher quality inpainting results with consistent and harmonious facial structures and appearance than existing methods and achieves the state-of-the-art performance, esp. for inpainting under-pose variations.

Digital Applications of Maxillofacial Reconstruction– A systematic review

Objectives: Many patients with maxillofacial defects require maxillofacial prosthetic rehabilitation due to cancer, trauma, or congenital diseases. Adequate surgical and prosthetic treatment planning is required to achieve satisfactory morphological and functional results. Before computer-aided design/computer-assisted manufacture (CAD/CAM) technology was introduced, conventional methods have been used to reconstruct the facial form, which involved making impressions, obtaining models and fabricating the prosthesis all of which is time consuming and requires multiple visits. A rapid progress has been made with advances in digital technology, such as milling systems, rapid prototyping, three-dimensional (3D) scanning, and 3D printing, which has improved the patients’ expectations, the functional and esthetic treatment outcomes. Materials and methods: An electronic search was conducted in the Cochrane, PubMed (MEDLINE), and ScienceDirect databases between July 2000 and October 2020. A manual search was also performed to cover all digital aspects of the maxillofacial prosthesis. The inclusion criteria were randomized clinical trials, prospective or retrospective cohort, and cross-sectional studies performed on humans with at least 1 year of follow-up and published within the last 20 years. Results: The results showed that the used technologies in a digital workflow of auricular, orbital and nasal prosthesis reduce the manufacturing time and allow the manufacture of high-quality prostheses for missing facial parts. The methodology provides a good position for further development issues and is usable for clinical practice. Conclusion: Utilization of digital technologies in the facial prosthesis manufacturing process can be a good contribution for higher patient comfort and production efficiency but also comes with a higher initial investment and greater demands for experience with software tools.

Enhanced global and local face feature extraction for effective recognition of facial emotions

AbstractEmotion recognition is a challenging task in the field of human computer interaction. For a successful human emotion recognition system, a robust, discriminative, and sensitive feature extraction is an essential need. In this article, the extraction of global features is done by the proposed frequency decoded lifting wavelet pattern descriptor (FDLWP) and extraction of local features is done by the proposed local gradient difference zig‐zag pattern descriptor (LGDZP). The face parts are detected using viola jones algorithm and the selection of optimal face active regions is accomplished by the calculation of structural similarity index measure. Eventually the local spatial zig‐zag structure of the face region is utilized to attain LGDZP descriptor. The fusion of local and global features is accomplished using canonical correlation analysis. The classification made using bank of restricted Boltzmann machine (RBM) classifiers yields promising results for the proposed method. Furthermore, the proposed recognition method delivers a promising accuracy in varying illumination, occlusion, and noise. The accuracy of the method is analyzed by doing experiments with the databases such as JAFFE, CK+, MMI, Oulu‐CASIA, and SFEW. The obtained results of the proposed method yield better accuracy than the existing state of art methods in this field.

Necrotizing Fasciitis: A Case Report. What to Do? When? And How?

Necrotizing fasciitis is one of the rare reported pathological condition in oral and maxillofacial region. It is characterized by rapidly progressive necrosis of fascia, with involvement of skin and muscles in late stages and can be fatal. We report a case of necrotizing fasciitis of neck and part of face, who presented to our institute with chief complaint of swelling since 15 days. The fascitis was found odontogenic in origin with initially involvement of buccal, and masseteric spaces. The condition was further complicated as patient had diabetes mellitus that prolonged the healing, favoring the environment for bacterial growth and necrosis. Drainage of the involved spaces followed by subsequent debridement of wound and reconstruction with skin graft was done. This report is addition to existing literature elaborating the treatment of necrotizing fasciitis in patient with comorbidity, in stages. What one should do? When and how?

Lack of affective priming indicates attitude-behaviour discrepancy for COVID-19 affiliated words

The ongoing novel coronavirus (COVID-19) pandemic has resulted in the enforcement of national public health safety measures including precautionary behaviours such as border closures, movement restrictions, total or partial lockdowns, social distancing, and face mask mandates in order to reduce the spread of this disease. The current study uses affective priming, an indirect behavioural measure of implicit attitude, to evaluate COVID-19 attitudes. Explicitly, participants rated their overall risk perception associated with contracting COVID-19 significantly lower compared to their perception of necessary precautions and overall adherence to public health measures. During baseline trials, participants explicitly rated COVID-19 affiliated words as unpleasant, similar to traditional unpleasant word stimuli. Despite rating the COVID-19 affiliated words as unpleasant, affective priming was not observed for congruent prime-target COVID-19 affiliated word pairs when compared to congruent prime-target pleasant and unpleasant words. Overall, these results provide quantitative evidence that COVID-19 affiliated words do not invoke the same implicit attitude response as traditional pleasant and unpleasant word stimuli, despite conscious explicit rating of the COVID-19 words as unpleasant. This reduction in unpleasant attitude towards COVID-19 related words may contribute towards decreased fear-related behaviours and increased incidences of risky-behaviour facilitating the movement of the virus.

Frontal tangential coronal view two-dimensional ultrasonography in assessment of fetal face [mouth and nose] in comparison with four-dimensional ultrasonography

BackgroundThe fetal face reflects strictly the development of the fetal brain during its growth. Four-dimensional (4D) examination permits continuous monitoring of the examined parts of fetal face and surface. The final performance of obstetric sonographic images depends upon multiple factors, such as fetal lie, uterine wall, abdominal wall fat, amniotic fluid, and the number of gestations which may limit the optimum performance of (4D) ultrasound. The two-dimensional (2D) ultrasound is the first choice due to its wide availability, low cost, and real-time capabilities. The tangential view obtained by (2D) ultrasound coronal sections through the face showed the nose, nostril, lips, eye, lens, and hard palate.ResultsOne hundred and sixty fetuses showed straight forwards obstetric examination by both 2D and 4D examinations with identical final reports. While the total number of fetuses with clear images by 2D frontal tangential coronal examination was 191 cases, only 29 cases failed, whereas 170 cases obtained clear images by 4D examination, and 50 cases failed. Both 2D and 4D ultrasound failed to obtain clear images of 19 cases, while 4D failed for 31 cases, and 2D failed for 10 cases. 2D imaging was found to be significantly better than 4D imaging, with a P value of 0.009.Conclusion2D ultrasound using the frontal tangential coronal view is an essential part of the fetal examination and more superior than 4D ultrasound in assessing facial anatomy and anomalies, as well comparable to 4D ultrasound as regards fascial expression.

Housing policy, shortage and socio-spatial segregation in the city of Piracicaba (SP)

The objective of the paper is to evaluate the close relationship between public policies of social housing, housing deficit and the process of socio-spatial segregation in the city of Piracicaba, located in the interior of the state of São Paulo, in Brazil, between the years 2000-2020. For this, the method adopted consists of a mixed quali-quantitative approach. Whether due to lack or inadequacy, the housing problem is one of the main urban shortages, whose measurement can be made from the deficit and household inadequacy indicators. To address these issues, housing policies are presented as a state intervention to provide access to housing. However, after years of implementing these policies, the paradox created by them is noted, since, although they were able to face part of the problem, they contributed to the intensification of socio-spatial segregation on the intra-urban scale. In these terms, Piracicaba is an emblematic case of the relationship that is established territorially between these elements, in which socio-spatial segregation is seen as a by-product of social housing policies, under the pretext of coping with housing needs. The investigation therefore seeks to contribute to research on the relationship between housing policies and socio-spatial segregation in medium-sized cities in São Paulo, inserting the concept of deficit as a basic element of this problem.

Property Risk Assessment for Expansive Soils in Louisiana

The physical properties of soil can affect the stability of construction. In particular, soil swelling potential (a term which includes swelling/shrinking) is often overlooked as a natural hazard. Similar to risk assessment for other hazards, assessing risk for soil swelling can be defined as the product of the probability of the hazard and the value of property subjected to the hazard. This research utilizes past engineering and geological assessments of soil swelling potential, along with economic data from the U.S. Census, to assess the risk for soil swelling at the census-block level in Louisiana, a U.S. state with a relatively dense population that is vulnerable to expansive soils. Results suggest that the coastal parts of the state face the highest risk, particularly in the areas of greater population concentrations, but that all developed parts of the state have some risk. The annual historical property loss, per capita property loss, and per building property loss are all concentrated in southeastern Louisiana and extreme southwestern Louisiana, but the concentration of wealth in cities increases the historical property loss in most of the urban areas. Projections of loss by 2050 show a similar pattern, but with increased per building loss in and around a swath of cities across southwestern and south-central Louisiana. These results may assist engineers, architects, and developers as they strive to enhance the resilience of buildings and infrastructure to the multitude of environmental hazards in Louisiana.

DualFace: Two-stage drawing guidance for freehand portrait sketching

Special skills are required in portrait painting, such as imagining geometric structures and facial detail for final portrait designs. This makes it a difficult task for users, especially novices without prior artistic training, to draw freehand portraits with high-quality details. In this paper, we propose dualFace, a portrait drawing interface to assist users with different levels of drawing skills to complete recognizable and authentic face sketches. Inspired by traditional artist workflows for portrait drawing, dualFace gives two-stages of drawing assistance to provide global and local visual guidance. The former helps users draw contour lines for portraits (i.e., geometric structure), and the latter helps users draw details of facial parts, which conform to the user-drawn contour lines. In the global guidance stage, the user draws several contour lines, and dualFace then searches for several relevant images from an internal database and displays the suggested face contour lines on the background of the canvas. In the local guidance stage, we synthesize detailed portrait images with a deep generative model from user-drawn contour lines, and then use the synthesized results as detailed drawing guidance. We conducted a user study to verify the effectiveness of dualFace, which confirms that dualFace significantly helps users to produce a detailed portrait sketch.