Single Face Research Articles

FD2Foremer: Thinking Face Forgery Detection in Midfrequency Geometry Details

Face forgery by DeepFake has caused widespread concern in community because of the synthesized media’s risks to the society. However, advances in recent years have been able to produce synthetic images indistinguishable from real images in the RGB space. Extracting midfrequency facial geometry details, including person-specific details and dynamic expression-dependent ones on facial geometry surfaces, is a promising way to highlight forgery clues during face forgery detection. In this paper, we use 3D face reconstruction to generate the displacement map from a single input face image, which is able to represent middle and fine scale details by indicating signed distance from the point in UV space. The cropped face images can also provide eyes and mouse information, so we use face image and its displacement map to extract the image features. Besides, we save the computation cost and maintain competitive performance using a universal transformer architecture and introduce a manifold distillation strategy to train our model from a more complex transformer backbone. Extensive experiments on various public DeepFake datasets indicate the effectiveness of the extracted facial geometry details, and proposed method achieves competitive performance.

Challenges of Single Motherhood in Socio-Cultural Context: A Qualitative Study in Rural areas of Bangladesh

The growing number of divorces and separations induces the frequency of single mothers facing challenges in the mainstream socio-economic realities in Bangladesh. Past studies focused on structural aspects, economic hardship, and psychological issues of single mothers, mainly in the urban context. Nevertheless, the challenges in rural settings vary from urban single motherhood, and this aspect remains understudied. This qualitative narrative aims to analyse the challenges of single mothers in rural settings in Bangladesh. Twenty-eight in-depth interviews and ten key informant interviews were conducted using semi-structured interview guidelines. Thematic analysis identified four major themes of the rural single mothers’ challenges, social, economic, cultural, and psychological, leading a single mother to become dependent on other family members or relatives. The underlying factor of the challenges was the deprivation of property, patriarchal social structure, and social stigma. Rural single mothers face more complex challenges than urban ones because of the lack of income opportunities, insecurity, and self-dependencies. Findings will contribute to recommending and formulating a policy for the single mother considering the local realities of the rural single mothers in Bangladesh.

Experimental and Numerical Investigation of Energy Evolution Characteristic of Granite considering the Loading Rate Effect

In order to investigate the loading rate effect of energy evolution in granite, the indoor physical simulation test of single face fast unloading-three directions and five faces stress-vertical continuous loading under different loading rates was conducted using a new true triaxial rockbursttest system. The energy accumulation-dissipation-release characteristics in the process of rock deformation and failure were revealed. Based on the three-dimensional discrete element theory and the polycrystalline modeling technique (randomly generated Voronoi mineral grains), the entire process of rockburst inoculation-occurrence-development, as well as the energy evolution characteristics under true triaxial single face unloading conditions, were studied. The test results indicate that the energy transport and conversion of rock samples under different loading rates exhibit distinct stage characteristics. It can be divided into the initial energy accumulation stage, steady energy accumulation stage, rapid energy dissipation stage, and rapid energy release stage. With a rise in loading rate, the specimen in the process of energy accumulation is accompanied by energy dissipation, more external input energy, and elastic strain energy release amount into the kinetic energy of fragments, resulting in the rockburst phenomenon. As the loading rate increases, the elastic strain energy conversion rate (Ue/U) falls, while the dissipative energy conversion rate (Ud/U) increases. The higher the elastic strain energy conversion rate and the lower the dissipative energy conversion rate, the more serious the rockburst occurs. Numerical simulation results show that the entire process of rockburst inoculation-occurrence-development is successfully simulated using the crystal scale fine model (CSFM) considering the grain mineral composition. The ejection failure process can be divided into four stages, including grains ejection, rock spalling into plates, rock shearing into fragments, and rock fragments ejection. The relationships between the peak strength, elastic strain energy of rock samples, and loading rates are obtained, which is consistent with the laboratory test results. The high rate linear growth of kinetic energy evolution between the two inflection points can provide precursor information for rockburst prediction.

Speculations on the W-mass measurement at CDF* *J.G. is supported by the National Natural Science Foundation of China (NNSFC) ( 12035008). Z.L. is supported in part by the U.S. Department of Energy (DOE) (DE-SC0022345). T.M. is supported by "Study in Israel" Fellowship for Outstanding Post-Doctoral Researchers from China, by PBC of CHE from India, and also partially supported by grants

The W mass determination at the Tevatron CDF experiment reported a deviation from the SM expectation at the 7σ level. We discuss a few possible interpretations and their collider implications. We perform electroweak global fits under various frameworks and assumptions. We consider three types of electroweak global fits in the effective-field-theory framework: the S-T, S-T-, and eight-parameter flavor-universal one. We discuss the amounts of tensions between different measurements reflected in these fits and the corresponding shifts in central values of these parameters. With these electroweak fit pictures in hand, we present a few different classes of models and discuss their compatibility with these results. We find that while explaining the discrepancy, the single gauge boson extensions face strong LHC direct search constraints unless the is fermiophobic (leptophobic), which can be realized if extra vector fermions (leptons) mix with the SM fermions (leptons). Vector-like top partners can partially generate the needed shift to the electroweak observables. The compatibility with the top squark is also studied in detail. We find that the non-degenerate top squark soft masses enhance the needed operator coefficients, enabling an allowed explanation compatible with current LHC measurements. Overall, more theoretical and experimental developments are highly in demand to reveal the physics behind this discrepancy.

Real-time fatigue driving detection system based on multi-module fusion

Fatigue driving is an important cause of traffic accidents, so it is particularly important to achieve effective fatigue driving detection. The current fatigue driving detection algorithm has the problems of poor comfort, vulnerable to external factors (light intensity, wearing masks and sunglasses), and poor real-time performance. To solve these problems, we design a system for detecting driver’s facial features (FFD-System) and an algorithm for judging driver’s fatigue state (MF-Algorithm). In FFD-System, we designed three networks M1-FDNet, M2-PENet and M3-SJNet for driver face detection, head pose estimation and eye-mouth state judgment respectively. M1-FDNet is a lightweight face detection network. We design a new loss function to allow the network to output five key face points in addition to the output face. M2-PENet converts the regression problem into a classification problem, and the head pose can be estimated only by a single face image. M3-SJNet can use the global information of eyes and mouth to determine its state. Wearing sunglasses or masks while driving can cause the driver’s eyes or mouth to be obscured, which makes it impossible to accurately detect all of the driver’s facial features. MF-Algorithm can solve this problem well. MF-Algorithm uses the output of FFD-System to calculate single eye closure time, blinking frequency, eye closure rate, mouth opening frequency and head misalignment time, and combines these parameters to comprehensively judge the driver’s fatigue state. Thus, even if a single facial feature cannot be accurately identified, our algorithm can accurately determine the driver’s fatigue status based on other features. The experimental results show that the algorithm meets the requirements of real-time detection in terms of detection speed and accuracy.

Driver Fatigue and Distracted Driving Detection Using Random Forest and Convolutional Neural Network

Driver fatigue and distracted driving are the two most common causes of major accidents. Thus, the on-board monitoring of driving behaviors is key in the development of intelligent vehicles. In this paper, we propose an approach which detects driver fatigue and distracted driving behaviors using vision-based techniques. For driver fatigue detection, a single shot scale-invariant face detector (S3FD) is first used to detect the face in the image and then the face alignment network (FAN) is utilized to extract facial features. After that, the facial features are used to determine the driver’s yawns, head posture, and the opening or closing of their eyes. Finally, the random forest technique is used to analyze the driving conditions. For distracted driving detection, a convolutional neural network (CNN) is used to classify various distracted driving behaviors. Also, Adam optimizer is used to reinforce optimization performance. Compared with existing methods, our approach is more accurate and efficient. Moreover, distracted driving can be detected in real-time running on the embedded hardware.

Dilemmas of Children Raised by Single Parents in the Kingdom of Eswatini

Children raised by single parents face many challenges that can be social, psychological, cultural or economic, resulting in erratic behavior. This study explored the dilemmas of children raised by single parents in the Kingdom of Eswatini in Lubombo region. The study was undertaken in a transformative research paradigm, and the Participatory Action Learning Action Research (PALAR) design was used. This qualitative study employed in-depth interviews and focus group discussions to get views from single parents and children in single parenting, and the data were analysed through thematic analysis. A sample of six single parents and six children raised by single parents were selected from the Lubombo region using the purposive sampling technique. Informed by Sigmund Freud’s psychoanalytic theory, the findings of the study revealed that single parenting led to children feeling abandoned, rejected and unloved by the absent parent, which had social and psychological effects on the children. The study concluded that children raised by single parents face psychological, social, economic and cultural challenges. Most of them are fixated and need counselling. The study suggests, for Eswatini, that the Home Affairs Department, in partnership with relevant non-governmental organisations (NGOs) should make psychological counselling readily available for single parents and their children.

Super-hard (MoSiTiVZr)Nx high-entropy nitride coatings

High-entropy nitride coatings have become a promising alternative to traditional protective coatings due to their excellent mechanical properties, thermal stability, oxidation, and wear resistance. However, the design and fabrication of hard high-entropy nitride coatings remain a great challenge. In this study, (MoSiTiVZr)Nx high-entropy nitride coatings were deposited by reactive DC magnetron sputtering at different nitrogen flow. The chemical compositions, structures, hardness, damage-tolerance, friction and corrosion resistances of the coatings were investigated. With the increase of nitrogen content in the coatings, the phase structure transforms from an amorphous structure to a single face centered cubic structure, and all alloying elements form metal-nitrogen bonds. The coating with nitrogen content of 53.7 at% exhibits an ultrahigh hardness of 45.6 GPa and the best damage-tolerance and wear resistance. The coatings with low nitrogen contents show better corrosion resistance than 304 stainless steels, however, an increase in nitrogen content results in a slight decrease in corrosion resistance. The mechanism of the structures, mechanical properties and corrosion resistance of (MoSiTiVZr)Nx coatings are discussed in details.

Deep learning based single sample face recognition: a survey

Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. In some practical situations, each identity has only a single sample available for training. Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models. Therefore, in recent years, researchers have attempted to unleash more potential of deep learning and improve the model recognition performance in the single sample situation. While several comprehensive surveys have been conducted on traditional single sample face recognition approaches, emerging deep learning based methods are rarely involved in these reviews. Accordingly, we focus on the deep learning-based methods in this paper, classifying them into virtual sample methods and generic learning methods. In the former category, virtual images or virtual features are generated to benefit the training of the deep model. In the latter one, additional multi-sample generic sets are used. There are three types of generic learning methods: combining traditional methods and deep features, improving the loss function, and improving network structure, all of which are covered in our analysis. Moreover, we review face datasets that have been commonly used for evaluating single sample face recognition models and go on to compare the results of different types of models. Additionally, we discuss problems with existing single sample face recognition methods, including identity information preservation in virtual sample methods, domain adaption in generic learning methods. Furthermore, we regard developing unsupervised methods is a promising future direction, and point out that the semantic gap as an important issue that needs to be further considered.

The impact of sex chromosome trisomies (XXX, XXY, XYY) on gaze towards faces and affect recognition: a cross-sectional eye tracking study

BackgroundAbout 1:650–1000 children are born with an extra X or Y chromosome (47,XXX; 47,XXY; 47,XYY), which results in a sex chromosome trisomy (SCT). This international cross-sectional study was designed to investigate gaze towards faces and affect recognition during early life of children with SCT, with the aim to find indicators for support and treatment.MethodsA group of 101 children with SCT (aged 1–7 years old; Mage= 3.7 years) was included in this study, as well as a population-based sample of 98 children without SCT (Mage= 3.7). Eye gaze patterns to faces were measured using an eye tracking method that quantifies first fixations and fixation durations on eyes of static faces and fixation durations on eyes and faces in a dynamic paradigm (with two conditions: single face and multiple faces). Affect recognition was measured using the subtest Affect Recognition of the NEPSY-II neuropsychological test battery. Recruitment and assessment took place in the Netherlands and the USA.ResultsEye tracking results reveal that children with SCT show lower proportion fixation duration on faces already from the age of 3 years, compared to children without SCT. Also, impairments in the clinical range for affect recognition were found (32.2% of the SCT group scored in the well below average range). ConclusionsThese results highlight the importance to further explore the development of social cognitive skills of children with SCT in a longitudinal design, the monitoring of affect recognition skills, and the implementation of (preventive) interventions aiming to support the development of attention to social important information and affect recognition.

Changing Tune: Exploring Processes and Tensions in the Transition from Movement to Business

The evolution of movement-based communities is drawing increasing attention among both scholars and practitioners. In their pursuit of scale and profits, movements tend to face the dual goals of drawing mainstream participation while preserving authenticity. Prior research tends to focus on uncoordinated initiatives led by multiple actors engaged with market logics, thus providing a gamut of offerings where different members of the community partake. Those movements that instead attempt to channel their entire communities into single business initiatives face the challenge to accommodate the different – sometimes conflicting – needs of their members. Yet, these challenges remain theoretically and empirically underexplored. Hence, we investigate the process by which community-based movements turn into single businesses by developing a qualitative, longitudinal (2014 – 2021) analysis of Rockin’1000 – also known as “The Biggest Rock Band in the World” – which originated as a movement of rock enthusiasts, and later morphed into a fully-fledged enterprise. By deploying a new perspective at the intersection of research on movements and value propositions, we conceptualize a generalizable, four-phase process. We show that each milestone throughout the transition becomes source of specific tensions, which alter movements’ equilibrium, and triggers a process of members shakeout, which may result in the decline of the initiative if not adequately addressed.

High-performance tri-band graphene plasmonic microstrip patch antenna using superstrate double-face metamaterial for THz communications

Abstract Recently, graphene-patch antennas have been widely used in communication technology, especially in THz applications due to the extraordinary properties of graphene material. Herein, a graphene-based rectangular microstrip patch antenna is designed on an FR4 substrate material (ɛr = 4.3). A single and double-faced superstrate MTM is placed upon the radiating patch for di erent purposes, such as enhancing the overall antenna performance, protecting the patch from environmental jeopardies, and generating a multiband resonance frequency. A single face superstrate triangle SRR unit was used to produce a dual-band frequency at 3.5 and 4.331 THz. The S 11 of the dual-band structure is achieved to be −26.78 dB and −46.25 dB with a bandwidth of 400 GHz and 460 GHz, respectively. The double face superstrate MTM unit cell of the triangle SRR printed on the opposite face gives another resonant frequency, so, triple frequency bands of 2.32, 3.35, and 4.38 THz with a wide impedance bandwidth of 230, 520, and 610 GHz, were generated, respectively. The double-face superstrate MTM not only enhances the antenna performance but also generates another resonant frequency that could be used in the next 6G communications. The proposed antenna is designed and optimized using two commercial 3D full-wave software, CST Microwave Studio and Ansoft HFSS, to validate the results.

An HBIM Methodology for the Accurate and Georeferenced Reconstruction of Urban Contexts Surveyed by UAV: The Case of the Castle of Charles V

The potentialities of the use of the UAV survey as a base for the generation of the context mesh are illustrated through the experiments on the case study, the Crotone Fortress, proposing a systematic general methodology and two procedural workflows for the importation of the triangulated model, maintaining its real geographical coordinates, in the Autodesk Revit environment through a Dynamo Visual Programming script [VPL]. First, the texturisation of the mesh of the urban context was experimented with, using the real-sized photogrammetric orthoimage as Revit material; therefore, the reproduction of the discretised detailed areas of the urban context was tested. They were imported via Dynamo by reading the coordinates of the vertices of every single face that constitutes the triangulated model and associating to each of them the corresponding real colorimetric data. Starting from the georeferenced context of the photogrammetric mesh, nine federated BIM models were produced: the general context models, the detailed models and the architectural model of the fortress.

Single and Multiface Detection and Recognition System

Face detection has drawn the interest of numerous research groups because to its vast application in various domains such as surveillance and security systems, as human-computer interaction, and many more. Face identification is the important phase involving several factors such as lighting, facial expression, and ageing effects. It’s more tough as detection takes a lot of time to detect and distinguish a single face at a time. Moreover, most of the existing technology cannot accurately detect many faces simultaneously. This study therefore presents a system that can recognize and identify multiple face image simultaneously with various expressions. Face-recognition procedure consists of data gathering, face detection, extraction, and classification feature. The face dataset is obtained from 10 participants with varied backgrounds and expressions. Subsequently, the viola-jones technique together with threshold technique is utilized in face detection to detect face presents while removing the unnecessary background to reduce face recognition time processing further. The Principal Component Analysis (PCA) is then employed to extract features while maintaining as much information as possible from enormous image data set. After formulating each face’s representation, the classification process is considered to recognize the identities of users’ faces. Here, a non-parametric classifier i.e. Support Vector Machine (SVM) is applied in this process. Conclusively, the system is able to detect around 90 percent multi-face user in different conditions.

Application Of The Viola Jones Algorithm On Face Detection

The face is the central organ for human expression, recognition, and communication. The face consists of four very important sense organs, namely the nose, eyes, ears, and tongue. The human brain has the privilege of recognizing someone based on their face. Face Detection is used to find out faces or nonfaces in an image and this section is the first step in the face detection process so its presence is very vital. Face detection task is very easy for humans, but this task is very complicated for computers because there are several stages that must be passed to detect faces. Where in this study the author designed and made an application for the application of the viola jones algorithm on face detection by discussing Python programming and using the Mysql database. The application of the viola jones algorithm on face detection goes well. Faces can be recognized by the application and display face owner information. Where the test is divided into 5 (five) including lighting testing with a test percentage level of 66.67% of detected faces, testing distance testing of 100% of faces detected at a distance of 1-100 cm, testing the number of faces there are 6 (six) results detected with 2 (two) faces, where faces must be approximately 10 cm apart so that faces can be detected and no results are detected on 3 (three) faces. So it can be concluded that the application cannot capture all faces detected by the camera. different face testing percentage test 100% the application can detect different people's faces with a single face.

Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning

With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition.

Environmental Decay of Single Use Surgical Face Masks as an Agent of Plastic Micro-Fiber Pollution

Large numbers of Single Use Surgical-type Face Masks, used by the public as personal protective equipment during the 2020–2022 COVID-19 pandemic, have been lost or intentionally discarded and have entered the environment rather than the waste management stream. These masks, made from non-woven polypropylene fibers, will undergo environmental decay which will release fiber fragments as microplastics into the environment. While the photochemical process of the decay of polypropylene polymers (photo-oxidation) is well understood, and while there are numerous studies that investigate mask decay and micro-fiber shedding in laboratory settings, there are no observational data that describe the progress and speed of decay on polypropylene face masks in real-life environmental settings. This paper examines the breakdown of single use surgical-type face masks under natural conditions. Masks from three manufacturers were exposed to natural sunlight over a ten-week period and their state of decay was photographically recorded in situ at weekly intervals. Visible decay accelerated after three weeks, with masks made from thinner spunbond fabric decaying more rapidly. Among same-weight fabric, photo-oxidation affected fabric dyed light blue more than undyed fabric, leading to a total breakdown after six weeks. The results are novel as they demonstrate a differential decay between the spunbonded and the melt-blown fabric, which cracks and breaks down much faster due to thinner fibers of shorter length and the lack of thermal bonding points. The resultant extensive micro-fiber generation was accelerated by external physical forces such as wind. This experiment highlights the fact that municipal agencies have only a narrow window of time to remove stray face masks from the urban environment if micro-fiber pollution is to be prevented.

Monogenic features based single sample face recognition by kernel sparse representation on multiple Riemannian manifolds

This paper presents a novel method for single sample face recognition, characterized by the use of grayscale monogenic features of the face images, and the kernel sparse representation on multiple Riemannian manifolds. To indicate regional face discriminability, the multi-scale extended monogenic features are firstly locally extracted from different regions of an image, according to a specific face partition scheme, and then the intrinsic subspace of the feature vector set corresponding to each region is further extracted and modeled as a point of a Grassmann manifold. The congeneric local feature scheme is also exploited to entire image for the extraction of co-occurrence distributions of the grouped feature images of intersectional dimensions, based on a special binarization scheme applied to the resultant feature images. This derives the auxiliary marginal distribution-based descriptors residing on the closures of multiple multinomial manifolds. To train the kernel sparse representation classifier using the combined descriptors for a recognition task, the strategy of kernel alignment combining column L2-norm-based kernel matrix normalization are adopted for multiple kernel fusion, where the used kernels are all derived from Riemannian geometries of two types of manifolds. The superiority of our method is demonstrated through extensive experiments.

Physiological Effects of Single and Double Face Mask Use with Moderate and Vigorous Exercise

ABSTRACT Introduction/Purpose Exercise is associated with increased exhalation of infectious particles in respiratory disease, and face mask use has become routine during the coronavirus disease 2019 (COVID-19) pandemic to mitigate particle spread. As of February 2022, the U.S. Centers for Disease Control and Prevention (CDC) continues to recommend the use of double masking (i.e., cloth masks worn over a surgical mask) to minimize particle leakage seen with single-mask use. Studies to date have examined the effects of single masks on exercise safety and performance, but the potential added burden during exercise of an additional mask layer has not been evaluated. The purpose of our study was to compare the effects of single and double face mask use during moderate to vigorous exercise. Methods Fifteen healthy, physically active young adults (mean age = 21.1 ± 0.8 yr; 8 males and 7 females; V˙O2peak = 46.3 ± 11.5 mL·kg−1⋅min−1) completed the study. This included an initial assessment of V˙O2peak, with subjects returning for three exercise test sessions performed while wearing either a surgical face mask, a cloth face mask, or double masks (i.e., a cloth mask worn over a surgical mask) in a random order. Test sessions consisted of a progressive cycling protocol with 10-min stages at 40%, 60%, and 75% oxygen consumption reserve (VO2R). Assessments were performed after 5 and 10 min at each exercise intensity and included heart rate, respiratory rate, oxygen saturation, and ratings of perceived exertion and dyspnea. Results At exercise intensities from 40% to 75% VO2R, there were no significant differences between cloth masks, surgical masks, and double masking in the measured subjective and physiologic parameters. Conclusions When compared with surgical and cloth face masks in common use, for the outcomes measured in this study, double masking in accordance with CDC recommendations does not result in significant impairments during moderate to vigorous exercise up to 75% VO2R in healthy young adults.

Single Parents Competing in a Dual-Earner Society: Social Policy to Level the Playing Field

I examine the relative poverty risk among single-parent households in countries that have a large share of households with dual earners. Data from the Luxembourg Income Study (LIS) Database are used to analyze eighteen OECD countries in the period 1984 to 2010. I find that single parents face higher relative income poverty risks in countries with a large share of dual-earner households and that this higher risk of poverty is related to higher standards of living in those countries: higher standards of living have raised poverty thresholds, and single-parent incomes are less likely to reach those higher poverty thresholds. I also find that this overall pattern varied across institutional contexts: a rise of dual-earner households puts single parents at a disadvantage only in countries that have relatively low public expenditures on childcare and relatively low income transfer policies.