Showing papers on "Face (sociological concept) published in 2022"

A survey on sentiment analysis methods, applications, and challenges

Abstract: The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

The Great Lockdown

Abstract: It is being said that we should all be proud of the way we are confronting the Covid-19 pandemic. Rather than privileging profits and trade, Western societies have made the noble decision to save lives at all costs. Indeed, the logic that has prevailed is that accepting any trade-off between saving lives and saving the economy is an unacceptable and monstrous idea, which is why liberal democracies worldwide have chosen to shutter businesses and force people to self-quarantine in their homes as much and for as long as possible. A vast majority of citizens and political leaders deemed that acting otherwise would have been nothing else but pure moral bankruptcy. Is it, however, possible that Western societies have gone the wrong way by embracing this inherently basic and impoverished version of life? The reason why a significant majority of us are unable to see this truth is because of our refusal to accept death and the tragic essence of human life which is the result of the various cultural parameters we have grown accustomed to over the past decades that followed WWII. The Covid-19 pandemic has simply been the triggering factor that has allowed these factors to reinforce the full strength they wield on our understanding of life. Defined primarily by a fear of death, the desire to prolong life as much as possible and minimize the hurdles individuals have to face during their existence has created a beast that is, in appearance, reassuring to the fearful creatures we have become. This beast has asepticized societies that refute the tragic nature of life and are willing to hinder individuals’ freedom and what makes our existence inherently humane. However, without realizing it, this Leviathan that now takes the form of a "nanny state" has altered our nature from individuals able and encouraged to enjoy life to people whose only destiny is to simply survive for as long as possible, without any other purpose than to avoid anything that might jeopardize this objective.

A survey on sentiment analysis methods, applications, and challenges

Abstract: The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

Lightweight Convolutional Neural Network Model for Human Face Detection in Risk Situations

Abstract: In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model.

InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs

Abstract: Although generative adversarial networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent representation to map a random code to a photo-realistic image. In this work, we propose a framework called InterFaceGAN to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the properties of the facial semantics encoded in the latent space. We first find that GANs learn various semantics in some linear subspaces of the latent space. After identifying these subspaces, we can realistically manipulate the corresponding facial attributes without retraining the model. We then conduct a detailed study on the correlation between different semantics and manage to better disentangle them via subspace projection, resulting in more precise control of the attribute manipulation. Besides manipulating the gender, age, expression, and presence of eyeglasses, we can even alter the face pose and fix the artifacts accidentally made by GANs. Furthermore, we perform an in-depth face identity analysis and a layer-wise analysis to evaluate the editing results quantitatively. Finally, we apply our approach to real face editing by employing GAN inversion approaches and explicitly training feed-forward models based on the synthetic data established by InterFaceGAN. Extensive experimental results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable face representation.

Boosting fire safety and mechanical performance of thermoplastic polyurethane by the face-to-face two-dimensional phosphorene/MXene architecture

Abstract: • A black phosphorus-MXene@polydopamine nanohybrid was synthesized. • The TPU/BP-MXene@PDA nanocomposite simultaneously showed reinforcing and toughening effects towards TPU. • The TPU/BP-MXene@PDA nanocomposite could be achieved excellent thermal stability, mechanical properties and flame retardancy. • A synergistic flame-retardant mechanism between black phosphorus and MXene in the TPU were discussed and clarified. Black phosphorus (BP), as one of the most promising fillers for flame retarding polymer, has been seriously limited in practical application, due to the agglomeration and poor structural stability challenges. Here, the BP was modified by MXene and polydopamine (PDA) via ultrasonication and dopamine modification strategy to improve the structural stability and dispersibility in the matrix. Then, the obtained (BP-MXene@PDA) nanohybrid was employed to promote the mechanical performance, thermal stability, and flame retardancy of thermoplastic polyurethane elastomer (TPU). The resultant TPU composite containing 2 wt.% of BP1-MXene2@PDA showed a 19.2% improvement in the tensile strength and a 13.8% increase in the elongation at break compared to those of the pure TPU. The thermogravimetric analysis suggested that BP-MXene@PDA clearly enhances the thermal stability of TPU composites. Furthermore, the introduction of the BP-MXene@PDA nanohybrids could considerably improve the flame retardancy of TPU composite, i.e., 64.2% and 27.3% decrease in peak heat release rate and total heat release, respectively. The flame-retardant mechanisms of TPU/BP-MXene@PDA in the gas phase and condensed phase were investigated systematically. This work provides a novel strategy to simultaneously enhance the fire safety and mechanical properties of TPU, thus expanding its industrial applications.

Pluralities

Abstract: Plurality is a conceptual tool that makes diversity explicit. Its importance lies in the theoretical field of studies. Plurality encompasses social values, collective lifestyle, ethics, respect, justice, knowledge of various cultures, and the impact of physical and psychological suffering created by prejudice and discrimination. Understanding the different types of knowledge and the mechanisms that keep them concealed and that affect them can contribute to reducing prejudice, fear, and insecurity in the face of the unknown. A confrontation occurs in the presence of insecurity. Fear and anger emerge, and violence becomes an act of eliminating that which is considered threatening. Moreover, the contemporary context adds technology, competition, and speed to this equation, and the result impacts our lives, disconnects us, and anesthetizes our social and emotional bodies. As a principle of plurality, diversity is key to our lives. There is no survival without transformation. We and the world are in constant change. This involves action, learning, and both personal and social experience.

Repurposing face mask waste to construct floating photothermal evaporator for autonomous solar ocean farming

Abstract: Plastic waste caused by the extensive usage of face masks during COVID-19 pandemic has become a severe threat to natural environment and ecosystem. Herein, an eco-friendly approach to repurpose face mask waste for clean water production via solar thermal evaporation is proposed. By taking advantage of its interwind structure, face mask holds the promise to be an ideal candidate material for constructing photothermal evaporator. In-situ surface modifications are performed successively with polyvinyl alcohol and polypyrrole to improve its wettability and solar absorption (97%). The obtained face mask-based evaporator achieves significantly enhanced solar efficiency (91.5%) and long-term salt-rejection stability. The harvested clean water befits plant growing to enable farming on sea surface. A floating photothermal evaporation prototype is then developed to demonstrate autonomous solar ocean farming, with plants successfully cultivated over time. As such, the proposed strategy provides a promising solution towards ecological sustainability by tapping multiple benefits.

DeepFake Detection Based on Discrepancies Between Faces and Their Context

Abstract: We propose a method for detecting face swapping and other identity manipulations in single images. Face swapping methods, such as DeepFake, manipulate the face region, aiming to adjust the face to the appearance of its context, while leaving the context unchanged. We show that this modus operandi produces discrepancies between the two regions (e.g., Fig. 1). These discrepancies offer exploitable telltale signs of manipulation. Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context (e.g., hair, ears, neck). We describe a method which uses the recognition signals from our two networks to detect such discrepancies, providing a complementary detection signal that improves conventional real versus fake classifiers commonly used for detecting fake images. Our method achieves state of the art results on the FaceForensics++ and Celeb-DF-v2 benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.

Pseudo RGB-D Face Recognition

Abstract: In the last decade, advances and popularity of low-cost RGB-D sensors have enabled us to acquire depth information of objects. Consequently, researchers began to solve face recognition problems by capturing RGB-D face images using these sensors. Until now, it is not easy to acquire the depth of human faces because of limitations imposed by privacy policies, and RGB face images are still more common. Therefore, obtaining the depth map directly from the corresponding RGB image could be helpful to improve the performance of subsequent face processing tasks, such as face recognition. Intelligent creatures can use a large amount of experience to obtain 3D spatial information only from 2D plane scenes. It is machine learning methodology, which is to solve such problems, that can teach computers to generate correct answers by training. To replace the depth sensors by generated pseudo-depth maps, in this article, we propose a pseudo RGB-D face recognition framework and provide data-driven ways to generate the depth maps from 2D face images. Specially we design and implement a generative adversarial network model named “D+GAN” to perform the multiconditional image-to-image translation with face attributes. By this means, we validate the pseudo RGB-D face recognition with experiments on various datasets. With the cooperation of image fusion technologies, especially non-subsampled shearlet transform (NSST), the accuracy of face recognition has been significantly improved.

Anti-Forensics for Face Swapping Videos via Adversarial Training

Abstract: Generating falsified faces by artificial intelligence, widely known as DeepFake, has attracted attention worldwide since 2017. Given the potential threat brought by this novel technique, forensics researchers dedicated themselves to detect the video forgery. Except for exposing falsified faces, there could be extended research directions for DeepFake such as anti-forensics. It can disclose the vulnerability of current DeepFake forensics methods. Besides, it could also enable DeepFake videos as tactical weapons if the falsified faces are more subtle to be detected. In this paper, we propose a GAN model to behave as an anti-forensics tool. It features a novel architecture with additional supervising modules for enhancing image visual quality. Besides, a loss function is designed to improve the efficiency of the proposed model. After experimental evaluations, we show that the DeepFake forensics detectors are susceptible to attacks launched by the proposed method. Besides, the proposed method can efficiently produce anti-forensics videos in satisfying visual quality without noticeable artifacts. Compared with the other anti-forensics approaches, this is tremendous progress achieved for DeepFake anti-forensics. The attack launched by our proposed method can be truly regarded as DeepFake anti-forensics as it can fool detecting algorithms and human eyes simultaneously.

A real time face mask detection system using convolutional neural network

Abstract: In current times, after the rapid expansion and spread of the COVID-19 outbreak globally, people have experienced severe disruption to their daily lives. One idea to manage the outbreak is to enforce people wear a face mask in public places. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model for static and real time videos has been presented which classifies the images as "with mask" and "without mask". The model is trained and evaluated using the Kaggle data-set. The gathered data-set comprises approximately about 4,000 pictures and attained a performance accuracy rate of 98%. The proposed model is computationally efficient and precise as compared to DenseNet-121, MobileNet-V2, VGG-19, and Inception-V3. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.

EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model

Abstract: Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In this paper, we propose the Emotion-Aware Motion Model (EAMM) to generate one-shot emotional talking faces by involving an emotion source video. Specifically, we first propose an Audio2Facial-Dynamics module, which renders talking faces from audio-driven unsupervised zero- and first-order key-points motion. Then through exploring the motion model’s properties, we further propose an Implicit Emotion Displacement Learner to represent emotion-related facial dynamics as linearly additive displacements to the previously acquired motion representations. Comprehensive experiments demonstrate that by incorporating the results from both modules, our method can generate satisfactory talking face results on arbitrary subjects with realistic emotion patterns.

Study on Surrounding Rock Control and Support Stability of Ultra-Large Height Mining Face

Abstract: Surrounding rock control and support stability in the process of coal seam mining in ultra-large height mining face are the key to normal mine operation. In this study, the roof movement and deformation of an ultra-large height mining face are analyzed, and the working resistance of the ultra-large height mining face is obtained by introducing the equivalent immediate roof. By analyzing the coal wall spalling, the multiple positions of the spalling and the required support force of the support are obtained. At the same time, ultra-large height supports are more prone to instability problems. In this study, the stability of the ultra-large height supports was analyzed by establishing a mechanical model. The results show that: 1. The overturning limit angle of support has a hyperbolic relationship with the center of gravity. 2. Under the condition of ultra-large height, the increase in the base width of the bracket significantly improves the stability of the supports. 3. The sliding limit angle of support is positively correlated with the support load and the friction coefficient between the support and the floor. The above conclusions can provide guidance on the selection of supports and the adoption of measures to enhance the stability of the supports during use under ultra-large height conditions. The working resistance of the ultra-large height supports in the 108 mining face of the Jinjitan Coal Mine was monitored. The monitoring results show that: The average resistance of the supports is 22.6 MPa. The selected supports can meet the stability requirements of the working face support. The frequency of mining resistance in 0~5 MPa accounts for 28.38%, which indicates that some supports are insufficient for the initial support force during the moving process. Furthermore, the stability of the supports can be enhanced by adjusting the moving process. This study provides a reference for the selection of supports in ultra-large height mining faces and proposes measures to enhance the stability of the supports, which provides guidance for the safe mining of coal in ultra-large height mining faces.

ElasticFace: Elastic Margin Loss for Deep Face Recognition

Abstract: Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks. All training codes, pre-trained models, training logs will be publicly released ¹.

A Novel and Efficient Method to Detect the Face Coverings to Ensurethe Safety using Comparison Analysis

Abstract: A face mask worn by a single person or a group of individuals can be identified using face mask detection, an AI-based system that examines a video stream. A confidence value is produced for each detection by our DeepSight programme. Either a person is labeled as "wearing a mask" or "not wearing a mask" depends on their classification. The World Health Organization (WHO) recommends that a face mask completely cover the face, including the chin and nose. Face Mask Detection System may be used with both CCTV cameras and already-installed USB or IP cameras. To distinguish between persons wearing masks and those who aren’t, it employs the most recent computer vision techniques. Compact model’s accuracy is really great, and it can tell if you’re wearing a mask properly or not (i.e.; mask covering the nose). Face mask detection data may be used to a wide range of settings and sectors, including corporate offices, shops, public transit, airports, and taxis. The main motive of the paper by the author is represented as solving the problem using cnn and transfer learning and also a comparison between both the approaches.

Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities

Abstract: The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.

Psychological resilience of entrepreneurs: A review and agenda for future research

Abstract: ABSTRACT Given that entrepreneurs face substantial adversity in initiating and developing new ventures, a burgeoning stream of research has sought to understand the concept of entrepreneurs’ psychological resilience. To structure and synthesize what we know about entrepreneurs’ psychological resilience, we systematically review the empirical literature to provide insights on how it has been conceptualized and operationalized, along with its key antecedents and outcomes. Based on our review, we advance a promising agenda for future research, grounded in connecting the psychological resilience of entrepreneurs to other research areas connected to the new venture development process. Overall, we point to the urgent need for theoretical precision to enhance the utility of empirical contributions, suggest promising research designs, expand on the important role of adversity, discuss potential boundary conditions, elaborate on the link between entrepreneurs’ psychological resilience and organizational resilience, and address the potential dark side of resilience.

Generative Adversarial Networks for Face Generation: A Survey

Abstract: Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style. These GAN-based approaches need to be reviewed, discussed, and categorized in terms of architectures, applications, and metrics. Several reviews that focus on the use and advances of GAN in general have been proposed. However, to the best of our knowledge, the GAN models applied to the face, which we call facial GANs, have never been addressed. In this article, we review facial GANs and their different applications. We mainly focus on architectures, problems, and performance evaluation with respect to each application and used datasets. More precisely, we review the progress of architectures and discuss the contributions and limits of each. Then, we expose the encountered problems of facial GANs and propose solutions to handle them. Additionally, as GAN evaluation has become a notable current defiance, we investigate the state-of-the-art quantitative and qualitative evaluation metrics and their applications. We conclude this work with a discussion on the face generation challenges and propose open research issues.