Human Face Research Articles

Mapping genes for human face shape: exploration of univariate phenotyping strategies.

Advancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.

Face repairing based on transfer learning method with fewer training samples: application to a Terracotta Warrior with facial cracks and a Buddha with a broken nose

In this paper, a method based on transfer learning is proposed to recover the three-dimensional shape of cultural relics faces from a single old photo. It can simultaneously reconstruct the three-dimensional facial structure and align the texture of the cultural relics with fewer training samples. The UV position map is used to represent the three-dimensional shape in space and act as the output of the network. A convolutional neural network is used to reconstruct the UV position map from a single 2D image. In the training process, the human face data is used for pre-training, and then a small amount of artifact data is used for fine-tuning. A deep learning model with strong generalization ability is trained with fewer artifact data, and a three-dimensional model of the cultural relic face can be reconstructed from a single old photograph. The methods can train more complex deep networks without a large amount of cultural relic data, and no over-fitting phenomenon occurs, which effectively solves the problem of fewer cultural relic samples. The method is verified by restoring a Chinese Terracotta Warrior with facial cracks and a Buddha with a broken nose. Other applications can be used in the fields such as texture recovery, facial feature extraction, and three-dimensional model estimation of the damaged cultural relics or sculptures in the photos.

Find the Gap: AI, Responsible Agency and Vulnerability

The responsibility gap, commonly described as a core challenge for the effective governance of, and trust in, AI and autonomous systems (AI/AS), is traditionally associated with a failure of the epistemic and/or the control condition of moral responsibility: the ability to know what we are doing and exercise competent control over this doing. Yet these two conditions are a red herring when it comes to understanding the responsibility challenges presented by AI/AS, since evidence from the cognitive sciences shows that individual humans face very similar responsibility challenges with regard to these two conditions. While the problems of epistemic opacity and attenuated behaviour control are not unique to AI/AS technologies (though they can be exacerbated by them), we show that we can learn important lessons for AI/AS development and governance from how philosophers have recently revised the traditional concept of moral responsibility in response to these challenges to responsible human agency from the cognitive sciences. The resulting instrumentalist views of responsibility, which emphasize the forward-looking and flexible role of agency cultivation, hold considerable promise for integrating AI/AS into a healthy moral ecology. We note that there nevertheless is a gap in AI/AS responsibility that has yet to be extensively studied and addressed, one grounded in a relational asymmetry of vulnerability between human agents and sociotechnical systems like AI/AS. In the conclusion of this paper we note that attention to this vulnerability gap must inform and enable future attempts to construct trustworthy AI/AS systems and preserve the conditions for responsible human agency.

AI-generated faces show lower morphological diversity than real faces do

Some recent studies suggest that artificial intelligence can create realistic human faces subjectively unrecognizable from faces of real people. We have compared static facial photographs of 197 real men with a sample of 200 male faces generated by artificial intelligence to test whether they converge in basic morphological characteristic such as shape variation and bilateral asymmetry. Both datasets depicted standardized faces of European men with a neutral expression. Then we used geometric morphometrics to investigate their facial morphology and calculate the measures of shape variation and asymmetry. We found that the natural faces of real individuals were more variable in their facial shape than the artificially generated faces were. Moreover, the artificially synthesized faces showed lower levels of facial asymmetry than the control group. Despite the rapid development of generative adversarial networks, natural faces are thus still statistically distinguishable from the artificial ones by objective measurements. We recommend the researchers in face perception, that aim to use artificially generated faces as ecologically valid stimuli, to check whether their stimuli morphological variance is comparable with that of natural faces in a target population.

Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study

Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person’s face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.

Toward Unity without Any Border

precis: The challenge and hope of ecumenism is to seek ways to see the reality of Christians who long for visible unity. Starting from the principle that this visible unity is already expressed in the unity of baptism, we aspire to the greatness of unity in the eucharist, the source of life. From every observable reality, the people of God earnestly seek an encounter with the sibling who suffers, despairs, and feels the desire for God. We believe in a God who wants and encourages the encounter, a God who has the initiative to see the face of humanity. We Christians live in many borderline situations—often at the limit of bloodshed, martyrdom, and suffering—for the simple desire to seek happiness and koinonia. The question would be: What do the rest of Christians do for those of my siblings who live in situations of death—borderline situations?

Image translation between human face and wayang orang using U-GAT-IT

<span lang="EN-US">Wayang orang performance is one of the Indonesian traditional cultures. The wayang orang players took about an hour to become a proper wayang orang since it takes time to have makeup and to find the appropriate costume before the performance is held. This problem can be solved by developing a computer-based simulation on applying makeup and traditional costume to the face and head of the wayang orang player, respectively. This task can be completed by using image translation. Therefore, people's images can be transformed into wayang orang images. This study aims to translate human faces into wayang orang by adding makeup and accessories using the U-GAT-IT with an unpaired dataset consisting of 1216 data trains and 240 data tests. The challenge of this research is to maintain the image background and the facial identity component in the input image. This research employs quantitative testing employ Kernel Inception Distance (KID), <em>Frèchet</em> Inception Distance (FID), and Inception Score (IS) to evaluate the quality of the output image obtained from the generator. The experimental results show that U-GAT-IT produces a better result than DCLGAN does according to the value of IS, FID, and KID. The IS, FID, and KID obtained by implementing U-GAT-IT are 2.414, 0.924, and 4.357, respectively.</span>

The Role of Genuine Human Faces and Social Presence in Charitable Websites

Abstract In the digital age, charitable organizations increasingly rely on websites as crucial platforms for fundraising, outreach, and communication. Integrating human traits to digital interfaces as a mean to improve the user’s attitude and experience is a well-researched topic with various case studies. This study investigates the impact of images featuring genuine human faces and perceived social scenarios on user engagement metrics within the context of charitable websites, focusing on trust perception, social media sharing behavior, and donation willingness. A controlled A/B/C test was conducted, presenting participants with three versions of a charitable website: one displaying a stack of books (Version A), another showcasing a stock image of children in school (Version B), and the third featuring a genuine photo of Romanian children engaged in learning activities (Version C). Data was collected from 150 respondents (50 for each version), and quantitative analysis was performed to evaluate the effectiveness of each background image in influencing user behavior. Results shown that authentic photographs evoke more trust and willingness to donate to a cause among Romanian respondents. The conclusions of this paper can be used to further improve the results of online crowdfunding efforts of charitable organizations.

Framework for multiple person identification using YOLOv8 detector: a transfer learning approach

Video surveillance extensively uses person detection and tracking technology based on video. The majority of person detection and classification techniques currently in use encounter challenges in video sequences brought on by occlusion, ambient lighting, and variations in human facial position. This paper proposed an effective person identification and classification system based on deep learning, which comprises a you only look once at version 8 (YOLOv8) detection and classification model, to classify human faces in video sequences accurately. This work proposes a new staff-detection and classification (S-DEC) dataset for comprehensive performance evaluation. visual tracker benchmark (VTB) standard database is used for performance comparison with the proposed S-DEC dataset. The proposed technique achieved 98.67% precision accuracy. For the S-DEC dataset, the system gave 94.67% accuracy in identifying facial images from a video sequence of 38 people addressing the pose variation occlusion challenge. Earlier methods used to provide approximately 85% to 90% results taking more execution time. Many existing techniques were successful in detecting people only-identification of the detected person has been done in limited papers. The proposed method uses the cross-stage partial connections (CSPDarknet53) model, integrated with YOLOv8, to achieve faster results. The proposed framework took 35 minutes to train a deep learning model. A testing time of 2 minutes ensured that the proposed framework outplayed other existing methodologies and successfully identified extra information about the detected person.

Optimal evolutionary framework-based activation function for image classification

Typically, supervised Machine Learning (ML)-based image classifiers leverage algorithms derived from either Artificial Neural Networks (ANNs) or optimal separating hyperplane (OSH)-based algorithms. However, despite recent progress has been made to enhance ANNs’ classification performance via the Rectified Linear Unit (ReLU)-based activation functions (AFs), there is currently no AF that scales across and benefit both ANNs and OSH-based classifiers. Moreover, the lack of globally optimal AFs leads to a high variance in image classification-related results. Thus, this study seeks to overcome this limitation by implementing a next-generation evolutionary framework (‘ActiGen’) to generate a novel and more reliable AF, which can scale to two families of AFs for two classifiers. The proposed evolutionary knowledge-based framework leverages a Multi-Objective (MO) optimisation method based on Genetic Algorithms (GA), or ‘MOGA’, to improve the generalisation of such classifiers. This evolutionary framework and its generated AF are validated using nine open-access datasets: seven image-based datasets, consisting of 22,136 images in total, and two large (561 features for 10,929 instances, 124 features for 1,700 instances) tabular datasets. These diverse datasets include both binary and multi-class classification, such as images of breast masses, those acquired via cardiac computed tomography, photos of famous people from the Internet, images of handwritten digits and those drawn on a graphics tablet, human faces with different lighting, details, and expressions, smartphone-related data captured during various activities and postural transitions, and clinical data on complications of myocardial infarction. Findings demonstrate that the proposed evolutionary optimisation framework (‘ActiGen-MOGA’) was able to generate a novel scalable AF, which led to achieve the highest classification performance and the fastest convergence across six out of nine datasets. In the best classification task, the ActiGen-MOGA-based AF led to a classification performance of 80 % and 78 % higher than the polynomial and Rectified Linear Unit (ReLU) AFs respectively.

Confusion and Diffusion Techniques for Image Encryption Process Based on Chaos System

Face recognition uses biometric technologies to identify humans based on their facial characteristics. This method is commonly used to restrict information access control. The benefits of face recognition systems encompass their ease of use and security. The human face recognition process consists of face detection, face tracking, and face recognition. The process uses some algorithms: the Viola-Jones for face detection, the Kanade-Lucas-Tomasi (KLT) for face tracking, and the principal component analysis (PCA) for face recognition. Furthermore, this research proposed face recognition with an encryption process to protect the data stored in a database. The encryption process consists of two main processes: confusion and diffusion. The confusion process is randomizing the position of the original image pixels. This research utilized the Arnold’s cat map (ACM) for the confusion process, and the diffusion process was performed using the XOR operation with the key generated from the 1D chaos system. Three different 1D chaos systems, namely logistic map, Bernoulli map, and tent map, were compared to see which chaos system had the best encryption results. Tests were conducted by comparing various parameters on the three proposed 1D chaos systems, including correlation coefficient, histogram analysis, entropy value, number of pixel rate changes (NPCR), and unified average change intensity (UACI). Based on testing the image encryption results, the diffusion process utilizing the tent map produced the best image encryption compared to other chaotic systems.

EmoTunes: Your Emotional Soundtrack

The human face is an important organ of an individual’s body and it especially plays an important role in extraction of an individual‘s behavior and emotional state. Manually segregating the list of songs and generating an appropriate playlist based on an individual’s emotional features is a very tedious, time consuming, labor intensive and upheld task. Various algorithms have been proposed and developed for automating the playlist generation process.However the proposed existing algorithms in use are computationally slow, less accurate and sometimes even require use of additional hardware like EEG or sensors. This proposed system based on facial expression extracted will generate a playlist automatically thereby reducing the effort and time involved in rendering the process manually. Thus the proposed system tends to reduce the computational time involved in obtaining the results and the overall cost of the designed system, thereby increasing the overall accuracy of the system. Testing of the system is done on both user dependent (dynamic) and user independent (static) dataset. Facial expressions are captured using an inbuilt camera. The accuracy of the emotion detection algorithm used in the system for real time images is around 85-90%, while for static images it is around 98- 100%.The proposed algorithm on an average calculated estimation takes around 0.95-1.05 sec to generate an emotion based music playlist. Thus, it yields better accuracy in terms of performance and computational time and reduces the designing cost, compared to the algorithms used in the literature survey. Keywords—Audio Emotion Recognition, Music Information Retrieval, Emotion Extraction Module, Audio Feature Extraction Module, Artificial Neural Networks, Confusion Matrix, Viola and Jones Face Detection

Factors Affecting the Waste of Selected Agricultural Products with an Emphasis on the Marketing Mix

Unusual levels of agricultural product waste are becoming one of the issues and dangers that human societies face in their efforts to achieve food security. Therefore, reducing agricultural product waste is one of the main strategies for the optimal use of production resources and support food security. In this study, a multilevel Bayesian technique was used to examine the characteristics of customers and the effects of marketing mix on the waste of selected agricultural products—a subgroup of fruits and vegetables in Mashhad, Iran. Based on this, 368 consumers (at the first level), 53 fruit and vegetable markets (at the second level), and 3 main supply centers of fruit and vegetables in the city (at the third level) were evaluated using the Bayesian multilevel model. The results showed that approximately 56% of food waste variance was caused by differences between consumers, 29% is due to the differences between fruit and vegetable markets, and almost 14% is due to the differences between the main supply centers of Mashhad. Also, the effects of the marketing mix showed that the place of distribution of agricultural products always has an increasing effect on the waste of agricultural products. Moreover, increasing the price of agricultural products reduces waste by consumers and keeps the consumer away from unnecessary purchases. The product factor also has an increasing effect on the waste of agricultural products, and consumers are encouraged to consume more and create more waste. A good way to reduce agricultural product waste is to use solutions that slow down the spoilage process and extend the shelf life of fruit and vegetables. Using an appropriate marketing mix and considering the characteristics of consumers can also control the waste of agricultural products.

INTRUDER DETECTION SYSTEM USING MACHINE LEARNING

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software. Keywords: Digital Image Processing, Face Detection, Face Recognition, Motion Detection

Generating Human Face with Dcgan and Gan

Generative Adversarial Networks (GANs) are prominent in unsupervised learning for their exceptional data-generation capabilities. GANs utilize backpropagation and a competitive process between a Generative Network (G) and a Discriminative Network (D). In this setup, G generates artificial images while D distinguishes real from artificial ones, enhancing G's ability to create realistic images. Deep Convolutional Generative Adversarial Networks (DCGAN) are particularly notable, using a convolutional architecture to produce high-quality human face images. This study trains DCGAN on the CelebFaces Attributes Dataset (CelebA), demonstrating its ability to generate human faces from unlabeled data and random noise. Evaluation is done quantitatively using the Structural Similarities Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) to assess image quality. Additionally, this abstract will compare the effectiveness of GANs and DCGANs in human face generation.

Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems.

Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (≤70 °C) solution process for fabricating DUV synapses based on PEA2PbBr4/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.

Attendance Management System Using Face Recognition

In the present academic system, regular class attendance of students’ plays a significant role in performance assessment and quality monitoring. The conventional methods are practiced in most of the institutions are by calling nadata reliability. The system is developed by the integration of ubiquitous components to make a portable device for managing the student’s attendance using Face Recognition technology. An automated attendance system for human face recognition in real time background for a college to mark the attendance of their employees and students. So Smart Attendance using Face Recognition is a real world solution which comes with day to day activities of handling employees. Here multiple user faces are detected and recognized with the trained data base multiple texture-based features.

Face Gender Classification using Combination of LPQ-Self PCA

The age factor had a significant impact on human faces, potentially influencing the performance of existing gender classification systems. This research proposed a new method that combined local descriptors such as Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) with Self-Principal Component Analysis (Self-PCA) as a feature extraction technique. The use of Self-PCA was chosen for its ability to address the age factor in human facial images, while also leveraging local descriptors to capture features from these images. The primary focus was to compare the performance of Self-PCA with LPQ+Self-PCA, along with the additional comparison of LBP+Self-PCA, in the task of gender classification using facial images. Euclidean distance served as the classifier, and the evaluation was conducted using the FG-Net and ORL datasets. The combination of LPQ+Self-PCA showed an improvement in accuracy by 57.85% compared to the combination of LBP+Self-PCA, which provided an accuracy of 56.47%. Meanwhile, using Self-PCA alone gave an accuracy of 55.37% on the FG-Net. In contrast, on the ORL dataset, both combinations gave the same accuracy result as Self-PCA, which was 90.14%, for images without blurring.

Waiting at Giurgiulesti Customs: Borders, Bureaucracy, and Geopolitics in between Ukraine, Moldova, and the EU

ABSTRACT Customs and geopolitics condition one another and are produced on a transnational, national, and local scale. The Russian war in Ukraine gives this interdependency a human face in the form of thousands of lorry drivers waiting to be cleared at external EU borders. This article explores the reasons and the inherent power structure behind movement and stasis at the border triangle between Ukraine, Moldova, and Romania. Flanking the European Union, this border is a historically contested and infrastructurally dense nodal point where corruption is rife. Analysing EU policy discourse on border management, and combining semi-structured interviews with customs officials, conversations with lorry drivers, and border and office-space ethnography, this article brings together different narratives of Moldova’s integration into the EU customs space. All these perspectives articulate cross-border bureaucracy through a geopolitical lens. Thus, this article ethnographically debunks the techno-bureaucratic discourse of customs regulation that emphasises efficiency, rationality, and transparency by shedding light on the everyday dimension of customs and geopolitics as a lived practice.

Multisensory contributions to skin-cosmetic product interactions.

The human face is one of the most salient regions of the body surface. Ratings of facial attractiveness, as well as judgements of a person's age, are influenced by the appearance of facial skin (not to mention the presence/absence of wrinkles). Unsurprisingly, many consumers spend huge amounts of money on trying to protect, maintain, and/or enhance their facial appearance. As highlighted by the evidence presented in this narrative review, both the skin and the cosmetic products that many consumers use are fundamentally multisensory in nature. The complex interaction between the particular skin site stimulated and the multisensory attributes of the product (e.g., when it is applied) can exert a number of effects on an individual's mood, their emotions, as well as on their self-perception (and self-confidence), over-and-above any functional effects that the cream or lotion may have on the skin itself. In this narrative historical review, the literature on the multisensory perception of facial skin is summarized and critically evaluated. Multisensory interactions taking place between the cosmetic product, its packaging, as well as its use/application at the sensory, cognitive, and emotional levels are all discussed.