Facial Information Research Articles

On the public provision of positional goods

Abstract We investigate whether the public provision of positional goods can be a sensible instrument to address inefficiencies arising from relative-standing externalities associated with the excessive consumption of such goods. In situations where consumers face a discrete choice between a private and a public alternative, providing the latter for free or at a subsidized rate generates incentives to opt for the public alternative. This allows to reduce excessive consumption. We show that such policies can increase welfare and characterize situations where they can even implement efficiency. Efficiency can typically be achieved if the non-positional utility component is sufficiently important. Moreover, we investigate how the public provision of positional goods may be a useful policy instrument in second-best situations, where either the government is constrained to rely on distortionary taxes, or where it redistributes facing information constraints.

HPG-GAN: High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network

To address the problems of low resolution, compression artifacts, complex noise, and color loss in image restoration, we propose a High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network (HPG-GAN). This mainly consists of Coarse Restoration Sub-Network (CR-Net) and Fine Restoration Sub-Network (FR-Net). HPG-GAN extracts high-quality structural and textural priors and facial feature priors from coarse restoration images to reconstruct clear and high-quality facial images. FR-Net includes the Facial Feature Enhancement Module (FFEM) and the Asymmetric Feature Fusion Module (AFFM). FFEM enhances facial feature information using high-definition facial feature priors obtained from ArcFace. AFFM fuses and selects asymmetric high-quality structural and textural information from ResNet34 to recover overall structural and textural information. The comparative evaluations on synthetic and real-world datasets demonstrate superior performance and visual restoration effects compared to state-of-the-art methods. The ablation experiments validate the importance of each module. HPG-GAN is an effective and robust blind face deblurring and restoration network. The experimental results demonstrate the effectiveness of the proposed network, which achieves better visual quality against state-of-the-art methods.

Exploring the Influence of Context on Emotional Mimicry and Intention: An Affirmation of the Correction Hypothesis.

Emotional mimicry, a phenomenon frequently observed in our everyday interactions, is the act of replicating another individual's facial expression. The Emotion Mimicry in Context View and the Correction Hypothesis underscore the critical role of context and intention within emotional mimicry. In two distinct studies, participants were presented with facial expressions of models (happiness and anger) within various contexts (affiliative, distancing, and neutral). Concurrently, we recorded electromyography (EMG) to index emotional mimicry, while participants explicitly rated the models' intentions. We observed context swiftly influences emotional mimicry within 500 ms, notably when the intentions of contexts are opposing to the intentions of facial expressions, leading to weakened muscle responses and diminished perceived intention. Furthermore, a notable correlation was discovered in the mimicry of angry faces; the more distancing the context, the stronger the corrugator supercilii (CS) muscle activity after context processing. First, emotional mimicry should not be simply viewed as an output corresponding to the expresser's facial expressions but the dynamic process involving the active participation of the observer. Second, intention serves as a pivotal anchor, effectively integrating facial and contextual information. As such, we provided empirical support for the Correction Hypothesis.

Prediction of Impulsive Aggression Based on Video Images.

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject's face and uses imaging photoplethysmography (IPPG) technology to obtain the subject's heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject's facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers.

COBIT 2019 INFORMATION SECURITY FOCUS AREA IMPLEMENTATION FOR REINSURCO DIGITAL TRANSFORMATION

As information technology (IT) advancement evolves in Indonesia's insurance sector, organizations like ReinsurCo must accelerate their digital transformation (DT) to remain competitively viable. Although DT paves the way for new business models and operational improvements, the implementation often fails due to poor IT governance. Under the supervision of the State-Owned Enterprises Agency (SOE) and the Financial Services Authority (FSA), ReinsurCo must comply with regulations stating that SOEs must independently assess IT maturity to ensure information security. This research utilizes the five stages of Design Science Research (DSR): problem explication, requirement specification, design and development, demonstration, and evaluation. Data was collected through semi-structured interviews and both internal and external document triangulation. The data were then analyzed using the COBIT 2019 Information Security framework, implementing design factors prioritizing information technology governance and management (ITGM) objectives: APO13 Managed Security, DSS05 Managed Security Services, and BAI06 Managed IT Changes. Further analysis and identification were conducted to discover gaps against the seven component capabilities. These identified gaps were mapped into people, process, and technology aspects, which led to the creation of essential improvement recommendations. These recommendations were compiled into an implementation roadmap that can serve as a priority guide for ReinsurCo. This research is expected to provide a knowledge base for prioritizing information security management in supporting DT by implementing the COBIT 2019 Information Security framework. In a practical context, it aids ReinsurCo in controlling its strategic plans to face information security challenges. Furthermore, this study also offers extensive benefits to the insurance industry.

DSNet: Dual-stream multi-scale fusion network for low-quality 3D face recognition

3D face recognition (FR) has become increasingly widespread due to the illumination invariance and pose robustness of 3D face data. Most existing 3D FR methods can only achieve excellent performance on complete and smooth faces. However, low-quality 3D FR with missing facial features still suffers from insufficient discriminative feature extraction for visible face regions. In this paper, we propose a dual-stream multi-scale fusion network (DSNet) for low-quality 3D FR. First, in the first stream, we design a new multi-scale local and global feature fusion network, which consists of an enhanced shallow feature extraction module, an enhanced deep feature extraction module, and a layered multi-scale feature correlation fusion module, aiming to obtain more discriminative details and category information of the facial visible region, reducing the interference of similar features and the redundancy of the same features. Second, we also introduced a capsule network as the second stream to enhance the expression of 3D facial spatial position information, thereby further improving the performance of low-quality 3D FR with missing facial features. We conduct extensive experiments on low-quality datasets (Lock3DFace, KinectFaceDB, and IIIT-D) and cross-quality datasets synthesized by Bosphorus. These results show that our proposed DSNet can achieve state-of-the-art recognition performance and exhibit excellent performance on low-quality 3D faces with missing facial features.

Parametric study of N170 sensitivity to diagnostic facial information during face identification

Parametric study of N170 sensitivity to diagnostic facial information during face identification

Facial expression of pain: Sex differences in the discrimination of varying intensities.

It has been proposed that women are better than men at recognizing emotions and pain experienced by others. They have also been shown to be more sensitive to variations in pain expressions. The objective of the present study was to explore the perceptual basis of these sexual differences by comparing the visual information used by men and women to discriminate between different intensities of pain facial expressions. Using the data-driven Bubbles method, we were able to corroborate the woman advantage in the discrimination of pain intensities that did not appear to be explained by variations in empathic tendencies. In terms of visual strategies, our results do not indicate any qualitative differences in the facial regions used by men and women. However, they suggest that women rely on larger regions of the face that seems to completely mediate their advantage. This utilization of larger clusters could indicate either that women integrate simultaneously and more efficiently information coming from different areas of the face or that they are more flexible in the utilization of the information present in these clusters. Women would then opt for a more holistic or flexible processing of the facial information, while men would rely on a specific yet rigid integration strategy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The computational value of face information sampled by super-recognizers

The computational value of face information sampled by super-recognizers

The Optimization of Face Detection Technology Based on Neural Network and Deep Learning

Face detection is a biometric technology that automatically contains facial feature information. It integrates digital image processing, pattern recognition, and other technologies and collects images or video streams containing human faces by cameras or cameras for automatic detection and tracking. Starting from the idea of local features and deep learning, aiming at the problem that traditional convolutional neural network (CNN) only extracts features from the whole image and ignores practical local details, this article proposes a deep CNN model based on the fusion of global and local features. It explores the face detection algorithm with better performance under the interference of illumination, expression, and other internal or external factors. This method designs a suitable network structure according to the size of the training data set, and the core technology is the debugging of super parameters. The simulation results show that compared with SVM, the improved CNN has obvious advantages in the later stage of operation, and the error is reduced by 36.85%. Compared with the traditional face detection method, it can automatically extract image features and also automatically learn its model and get a higher recognition rate.

The impact of stability in appearance on the development of facial representations

The way faces become familiar and what information is represented as familiarity develops has puzzled researchers in the field of human face recognition for decades. In this paper, we present three experiments serving as proof of concept for a cost-efficient mechanism of face learning describing how facial representations form over time and accounting for recognition errors. We propose that the encoding of facial information is dynamic and modulated by the intrinsic stability in individual faces' appearance. We drew on a robust and ecological method using a proxy of exposure to famous faces in the real world and manipulated test images to assess the prediction that recognition of famous faces is affected by their relative stability in appearance. We consistently show that stable facial appearances (like Tom Cruise's) facilitate recognition in early stages of familiarisation but that performance does not improve much over time. In contrast, variations in appearance (like Jared Leto's) hinder recognition at first but improve performance with further media exposure. This pattern of results is consistent with the proposed cost-efficient face learning mechanism whereby facial representations build on a foundation of large-scale diagnostic information and refine over time if needed. When coarse information loses its diagnostic value through the experience of variations in appearance across encounters, diagnostic facial details and/or their spatial relationships must receive more weights, leading to refined representations that are more discriminative and reliable than representations of stable faces.

Three's a crowd: Fast ensemble perception of first impressions of trustworthiness

Trustworthiness impressions are fundamental social judgements with far-reaching consequences in many aspects of society, including criminal justice, leadership selection and partner preferences. Thus far, most research has focused on facial characteristics that make a face individually appear more or less trustworthy. However, in everyday life, faces are not always perceived in isolation but are often encountered in crowds. It has been proposed that we deal with the large amount of facial information in a group by extracting summary statistics of the crowd, a phenomenon called ensemble perception. Prior research showed that ensemble perception occurs for various facial features, such as emotional expression, facial identity, and attractiveness. Here, we investigated whether observers can integrate the level of trustworthiness from multiple faces to extract an average impression of the crowd. Across four studies, participants were presented with crowds of faces and were asked to report their average level of trustworthiness with an adjustment (Experiment 1) and a rating task (Experiments 2 and 3). Participants were able to extract an ensemble perception of trustworthiness impressions from multiple faces. Moreover, observers were able to form a summary statistic of trustworthiness impressions from a group of faces as quickly as 250 ms (Experiment 4). Taken together, these results demonstrate that ensemble perception can occur at the level of impressions of trustworthiness. Thus, these critical social judgements not only occur for individual faces but are also integrated into a unique ensemble impression of crowds. Our findings contribute to the development of a more ecological approach to the study of trust impressions, since they provide an understanding of trustworthiness judgements not only on an individual level, but on a much broader social group level. Furthermore, our results drive forward new theory because they demonstrate for the first time that ensemble representations cover a broad range of phenomena than previously recognized, including complex high-level facial trait judgements such as trustworthiness impressions.

Gaze Estimation Method Combining Facial Feature Extractor with Pyramid Squeeze Attention Mechanism

To address the issue of reduced gaze estimation accuracy caused by individual differences in different environments, this study proposes a novel gaze estimation algorithm based on attention mechanisms. Firstly, by constructing a facial feature extractor (FFE), the method obtains facial feature information about the eyes and locates the feature areas of the left and right eyes. Then, the L2CSNet (l2 loss + cross-entropy loss + softmax layer network), which integrates the PSA (pyramid squeeze attention), is designed to increase the correlation weights related to gaze estimation in the feature areas, suppress other irrelevant weights, and extract more fine-grained feature information to obtain gaze direction features. Finally, by integrating L2CSNet with FFE and PSA, FPSA_L2CSNet was proposed, which is fully tested on four representative publicly available datasets and a real-world dataset comprising individuals of different backgrounds, lighting conditions, nationalities, skin tones, ages, genders, and partial occlusions. The experimental results indicate that the accuracy of the gaze estimation model proposed in this paper has been improved by 13.88%, 11.43%, and 7.34%, compared with L2CSNet, FSE_L2CSNet, and FCBA_L2CSNet, respectively. This model not only improves the robustness of gaze estimation but also provides more accurate estimation results than the original model.

Technology acquisition following inventor exit in the biopharmaceutical industry

In technological acquisitions, a “focal” firm aiming to appropriate the technological knowledge of another “alter” firm faces information asymmetries in imperfect strategic factor markets. Little is known about whether the mobility of people between the firms may reduce this information asymmetry and contribute to an increased likelihood of an acquisition. To investigate this question, we argue that for an actively acquiring firm, inventor-exit to an alter firm increases the likelihood of acquisition because it helps identify an acquisition target. In addition, since an acquiring firm is more likely to have information about a potential target with more technological capital, inventor-exit is less likely to reduce information asymmetries and to increase chances of an acquisition. Based on an analysis of acquisitions between firms in the period between 1993 and 2010 in the global biopharmaceutical industry, we find support for these arguments. For an active acquirer firm, inventor exit increases the likelihood of acquisition of the alter (hiring) firm by 335% as compared to an acquisition of randomly selected alter firm. Moreover, the positive effect of inventor–exit on the likelihood of acquisition is negatively moderated by the technological capital of the alter firm. A policy implication is to treat non-compete clauses with caution because they may impede the reduction of information asymmetry that follows from inventor-exit and reduce the likelihood of some acquisitions eventually.

4D facial analysis: A survey of datasets, algorithms and applications

Facial information plays an important role in human communication, e.g., rich and nuanced facial expressions effectively convey emotions. Traditional data such as 2D facial images and videos are susceptible to perturbation from lighting and occlusion, while 3D static data such as mesh models lack the temporal information which is necessary to describe facial dynamics. Therefore, researches on 4D facial data (3D facial models together with time as the fourth dimension) have received considerable attention in recent years. 4D data can simultaneously reflect the complex facial temporal and space information. To fully explore these characteristics, we present a systematic overview of the 4D facial research in this paper. We give a review in terms of historical development of 4D facial datasets, acquisition process of these datasets, related algorithms and applications, and discussion of outstanding issues. We also analyze the 4D facial research works by summarizing and comparing them; in particular, we present the results on three kinds of tasks conducted on the 4D facial datasets, i.e., expression-related tasks (AU detection, expression recognition and retrieval), generation tasks (3D facial reconstruction, facial expression and facial animation generation), and other tasks (facial registration, facial recognition and facial disease diagnosis). Finally, we summarize the future research directions.

Retracted: Application of Artificial Neural Network Algorithm in Facial Biological Image Information Scanning and Recognition.

[This retracts the article DOI: 10.1155/2022/1142682.].

Multimodal Emotion Recognition for Children with Autism Spectrum Disorder in Social Interaction

Autism Spectrum Disorders (ASD) remain a healthcare challenge and gain considerable attention due to the increasing prevalence rates and insupportable burden on families and society. It is noted that the recognition of children’s emotional states plays an important role in the evaluation and intervention process of ASD. In this paper, we aim to address the problem of automatic recognition of the emotional states of ASD children in social interactive scenarios. Since the child can be unconstrained in realistic scenarios, the face occlusion under pose variations and uncertain backgrounds become challenges of this task. To tackle this problem, we employ both facial expressions as well as body poses as cues to recognize the emotional states while most traditional methods only leverage the former. Firstly for the facial information, spatial features are extracted through convolutional neural networks followed by a temporal transformer to extract temporal information. Then for the body pose information, graph convolutional networks combined with the self-attention part are used to represent spatial features and temporal convolutional layers for temporal counterparts. Finally, different multimodal fusion ways are explored to generate final recognition results. We evaluate this method on a challenging database collected by us in real-world child-clinician interactive scenarios and the proposed method achieved significantly better results than baselines using only facial information. Thus it is suggested that there is a potential to assist in clinical practice by providing the recognized emotion as feedback.

Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression

Perception of facial expression is crucial for primate social interactions. This visual information is processed through the ventral cortical pathway and the subcortical pathway. However, the subcortical pathway exhibits inaccurate processing, and the responsible architectural and physiological properties remain unclear. To investigate this, we constructed and examined convolutional neural networks with three key properties of the subcortical pathway: a shallow layer architecture, concentric receptive fields at the initial processing stage, and a greater degree of spatial pooling. These neural networks achieved modest accuracy in classifying facial expressions. By replacing these properties, individually or in combination, with corresponding cortical features, performance gradually improved. Similar to amygdala neurons, some units in the final processing layer exhibited sensitivity to retina-based spatial frequencies (SFs), while others were sensitive to object-based SFs. Replacement of any of these properties affected the coordinates of the SF encoding. Therefore, all three properties limit the accuracy of facial expression information and are essential for determining the SF representation coordinate. These findings characterize the role of the subcortical computational processes in facial expression recognition.

Re: "Analysis of Online Videos on Facial Feminization Surgery: What Are Patients Watching on TikTok and YouTube?" by Ziltzer et al: Concerns Regarding Proliferation of Social Media (Especially TikTok) As a Source of Facial Feminization Surgery Information.

Re: "Analysis of Online Videos on Facial Feminization Surgery: What Are Patients Watching on TikTok and YouTube?" by Ziltzer et al: Concerns Regarding Proliferation of Social Media (Especially TikTok) As a Source of Facial Feminization Surgery Information.

Improving the Effectiveness and Efficiency of Web-Based Search Tasks for Policy Workers

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands.