Facial Imagery Research Articles

Implementation and validation of face de-identification (de-facing) in ADNI4.

Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants' privacy. An independent expert committee evaluated 11 face-deidentification ("de-facing") methods and selected four for formal testing. Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4. ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI. ADNI is implementing "de-facing" of MRI and PET beginning in ADNI4. "De-facing" alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

Enhanced rock mass rating prediction from tunnel face imagery: A decision-supportive ensemble deep learning approach

Ensuring safety in tunnel construction necessitates a rapid and objective evaluation of exposed tunnel faces for proactive rock mass risk assessment. The diversity of rock types and discontinuities contributes to structural complexity and local variability, introducing subjectivity and uncertainty in traditional evaluations based on the experience of field engineers. In this study, a convolutional neural network (CNN) is employed for regression to enhance the objectivity and consistency of rock mass classification via rock mass rating (RMR). The model interprets the entire tunnel face image holistically by identifying and learning from complex patterns, rather than detecting specific geological features like fractured or discontinuity zones. The network model, comprising feature extraction and regression blocks, utilizes the EfficientNet family for computational efficiency and accuracy. Data augmentation and ensemble learning address prediction variability due to image quality, optimizing the model for accurate RMR predictions and providing error ranges. The RMR predictions using excavation site images closely follow the field-evaluated evolution. A server communication-based mobile application is developed for real-time RMR evaluation, enhancing its practical field applicability. In geology, tunnel, and geotechnical engineering, where decisions rely on extensive experience, our approach demonstrates that deep learning can enhance decision-making by analyzing large accumulated datasets to predict optimal outcomes.

The impact of the Seller's facial image on consumer purchase behavior in peer-to-peer accommodation platforms

Consumers' initial impressions of a seller's facial appearance play a crucial role in influencing purchase behavior. However, the underlying mechanisms by which a seller's facial appearance influences consumer purchase behavior remain unexplored. This study adopted a multimethod approach to explore these questions. First, drawing on the profile pictures of 1240 sellers from the Airbnb peer-to-peer accommodation platform, we investigated the impact of two principal facial attributes (facial attractiveness and facial expressions) on the formation of consumers' initial impressions and their subsequent influence on purchase decisions. Second, we explored the underlying mechanisms through two online experiments. Our findings indicate a U-shaped relationship between sellers' facial attractiveness and consumer purchase behavior. Additionally, we found that facial expressions such as happy, surprise, and sad, influence consumer purchase behavior positively in comparison to neutral expressions. Furthermore, our study demonstrates that the purchase price acts as a moderator of the U-shaped effect of facial attractiveness on consumer purchase behavior, while text sentiment intensity serves as a negative moderator of the impact of facial expressions of consumer purchase behavior. Finally, the study elucidates the influence mechanism of facial attractiveness and facial expressions on consumers' purchasing decisions. These insights provide novel perspectives on the role of sellers' facial attributes and offer valuable references for devising effective marketing strategies and optimizing platform operational services related to the design of seller facial imagery.

A Cultural Analytic Study of Facial Imagery in Time Magazine, 1923–2014

We extracted and categorized images of human faces from a digitized Time magazine archive of issues published from 1923 to 2014. The data revealed several large-scale trends that are consistent with the historical context of the magazine. We found that faces are more likely to be smiling in the context of advertisements compared to other contexts, that women are more likely to be smiling than men in all contexts, and that women’s faces are more likely to be found in advertisements than men’s faces. We observed trends in the presence of racialized faces: African American faces increased more or less linearly, reaching approximately proportional representation at the turn of the twenty-first century, while the appearance of faces categorized as Asian peaked in 1970. We interpret these trends through a theoretical framework that connects them to larger events in American history.   Nous avons extrait et catégorisé des images de visages humains à partir des archives numérisées du magazine Time, dont les numéros ont été publiés entre 1923 et 2014. Les données ont révélé plusieurs tendances à grande échelle qui sont cohérentes avec le contexte historique du magazine. Nous avons constaté que les visages sont plus susceptibles d'être souriants dans le contexte des publicités que dans d'autres contextes, que les femmes sont plus susceptibles d'être souriantes que les hommes dans tous les contextes, et que les visages de femmes sont plus susceptibles d'être trouvés dans les publicités que les visages d'hommes. Nous avons observé des tendances dans la présence de visages racialisés : Les visages afro-américains ont augmenté plus ou moins linéairement, atteignant une représentation approximativement proportionnelle au tournant du XXIe siècle, tandis que l'apparition de visages catégorisés comme asiatiques a atteint son maximum en 1970. Nous interprétons ces tendances à l'aide d'un cadre théorique qui les relie à des événements plus importants de l'histoire américaine.

ENHANCING DEEPFAKE DETECTION: LEVERAGING MTCNN AND INCEPTION RESNET

In response to the escalating threat posed by manipulated facial imagery in the digital age, our research project is dedicated to developing an advanced framework for the detection and classification of such content, augmented by a proactive user reporting mechanism. Leveraging state-of-the-art deep learning models like Multi-Task Cascaded Convolutional Networks (MTCNN) and InceptionResnetV1, our framework achieves an impressive accuracy rate of 92% in distinguishing between genuine and manipulated faces. The integration of explainability methods such as Grad-CAM enhances model interpretability, empowering users to understand model predictions. Additionally, our user-centric reporting interface enables active user participation in identifying and flagging potentially manipulated content, fostering transparency and accountability in digital media platforms. With the continued proliferation of deepfake technology, our research endeavors not only advance facial image analysis techniques but also uphold principles of trust and integrity in the digital realm, aiming to safeguard the credibility of information dissemination through vigilance, innovation, and collaborative action. Keywords: Deepfake detection, Facial image manipulation, Deep learning models, User reporting mechanism, Model interpretability, Transparency in digital media, Trust and integrity, Information dissemination, Vigilance, Collaborative action.

Securing synthetic faces: A GAN-blockchain approach to privacy-enhanced facial recognition

In recent years, facial recognition technology has become increasingly integrated into society, making privacy protection crucial. Previous techniques offered minimal secrecy safeguards through simple obscuration methods. This paper addresses the strict privacy requirements of face image data by developing a novel framework that synergistically integrates Generative Adversarial Networks (GANs), clustering algorithms, and Blockchain technology. The methodology proposes a cutting-edge Privacy-Preserving Self-Attention GAN (PPSA-GAN) to generate realistic synthetic facial imagery. An integrated mini-batch K-means clustering algorithm anonymizes these images into distinct groupings, maximizing privacy preservation. Blockchain integration complements the system by fortifying trust through decentralized ledgers for transparent yet secure data storage and auditing. Rigorous benchmarking on the CelebA dataset confirms the PPSA-GAN architecture’s state-of-the-art performance, attaining an impressive Inception Score of 13.99 and a Fréchet Inception Distance of 35.50. The mini-batch clustering forms 125 distinct clusters, effectively anonymizing facial attributes within the synthetic images. Blockchain integration further bolsters privacy assurances via tamper-proof historical records, showcasing precision, recall, F1-score, and accuracy values of 0.948, 0.938, 0.943, and 0.947, respectively. This multifunctional framework represents a novel contribution, fostering an ethical technological ecosystem that balances progress and privacy. Prospective deployment horizons encompass identity verification, surveillance infrastructure, and augmentation of medical image repositories, seeding an enlightening future for facial recognition domains.

Multi-Angle Facial Recognition: Enhancing Biometric Security with a Broadly Positioned Stereo-Camera System

This study addresses the vulnerabilities of traditional monocular camera-based face recognition systems, emphasizing the need for improved security and reliability in biometric authentication under varying environmental conditions, lighting, and human poses. To counteract the risk of spoofing attacks using masks or static images, we introduce a multi-angle stereo camera system. This system is strategically designed to capture facial imagery from multiple perspectives, thereby enhancing depth perception and spatial accuracy, crucial for high-security authentication. Employing a novel image processing approach, the study integrates a Convolutional Neural Networks (CNN) with a simple Boolean operation to differentiate the landmarks detected on each camera. This method exploits CNN’s robust feature extraction capabilities and the effective usage of stereo camera, enabling precise detection and analysis of 3D facial landmarks. Such an approach significantly bolsters the system’s ability to differentiate between genuine faces and deceptive representations like masks or static images. Empirical results demonstrate that the stereo camera configuration substantially improves recognition accuracy, reducing both false positives and negatives, especially in controlled spoofing scenarios. The advanced 3D facial landmark detection further reinforces the system’s security. With its enhanced robustness and security, the developed system shows great potential for applications in areas requiring stringent identity verification, such as banking, public facilities, and smart home technologies.

Visual imagery deficits in posterior cortical atrophy

ABSTRACT Visual imagery has a close overlapping relationship with visual perception. Posterior cortical atrophy (PCA) is a neurodegenerative syndrome marked by early impairments in visuospatial processing and visual object recognition. We asked whether PCA would therefore also be marked by deficits in visual imagery, tested using objective forced-choice questionnaires, and whether imagery deficits would be selective for certain properties. We recruited four patients with PCA and a patient with integrative visual agnosia due to bilateral occipitotemporal strokes for comparison. We administered a test battery probing imagery for object shape, size, colour lightness, hue, upper-case letters, lower-case letters, word shape, letter construction, and faces. All subjects showed significant impairments in visual imagery, with imagery for lower-case letters most likely to be spared. We conclude that PCA subjects can show severe deficits in visual imagery. Further work is needed to establish how frequently this occurs and how early it can be found.

Investigation of gender bias in the mental imagery of faces

People tend to think of the prototypical person as a man more than as a woman, but this bias has primarily been observed in language-based tasks. Here, we investigated whether this bias is also present in the mental imagery of faces. A preregistered cross-cultural reverse-correlation study including participants from six WEIRD and non-WEIRD countries varying in gender equality (i.e., China, Ghana, Norway, Pakistan, Turkey, and the US; N = 645) unexpectedly suggested that people imagine the face of a generic “person” more as a woman than as a man. Replicating this unexpected result, a second preregistered study ( N = 115) showed that U.S. participants imagine the face of a typical person as being more similar to their imagined face of a woman than of a man. We discuss explanations for these unexpected findings, including the possibility that the prototypical person is male-biased—consistent with previous work—but the default face may be female-biased.

Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning

Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face.
 Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively.
 Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach.
 Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery.
 Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.

Perception of emotion in the facial expressions and body language of athletes

Facial expressions are commonly believed to reliably convey emotional information, but some research suggests that people are better at perceiving emotions through body language. We hypothesized that individuals' emotional perception would improve when body imagery was presented, relative to viewing the face alone. Following musical mood induction, participants were shown images of winning and losing tennis players that were cropped to show either (a) only the face, (b) only the body, or (c) both the face and the body, before rating each player's perceived level of arousal and emotional experience. Results showed there was a reciprocal emotional rating effect for face imagery, with participants mistakenly rating losing faces as experiencing more positive emotion than winning faces did; when body imagery was shown along with the face, participants' emotional perception was more accurate. Significant gender differences were observed in ratings of female versus male players. Our study indicates that without further context, emotional perception is unreliable from the face alone. Theoretical and practical implications are discussed.

Crowd Face Detection with Naive Bayes in Attendance System Using Raspberry Pi

PT. Restu Agung Narogong is a company with a total of 176 employees, queues often occur in the attendance process, both incoming and outgoing attendance. The employee needs to register their attendance. It is time consuming during the shift change. Therefore, a biometric system is needed to support the attendance system to identify employee without registering themselves. One of the alternative biometric systems is face recognition by using a computer vision. The purpose is to implement a crowd face detection with Raspberry Pi using the Naïve Bayes classifier. This system uses an algorithm to extract facial characteristics into mathematical data. Then the data is compared with data from other facial characteristics collected in the database. This device uses Python as a programming language with some of the scientific Python libraries. The testing of the Naïve Bayes method was conducted using a sample of dataset of 370 augmented facial imagery. The accuracy of this implementation is 76.31%, the precision is 78.25% and recall 81.25%. The background and lighting of the captured image affect the accuracy of this device.

Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer’s disease research

Brain imaging research studies increasingly use “de-facing” software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer’s Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)–Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.

Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition

Interest in thermal to visible face recognition has grown significantly over the last decade due to advancements in thermal infrared cameras and analytics beyond the visible spectrum. Despite large discrepancies between thermal and visible spectra, existing approaches bridge domain gaps by either synthesizing visible faces from thermal faces or by learning the cross-spectrum image representations. These approaches typically work well with frontal facial imagery collected at varying ranges and expressions, but exhibit significantly reduced performance when matching thermal faces with varying poses to frontal visible faces. We propose a novel Domain and Pose Invariant Framework that simultaneously learns domain and pose invariant representations. Our proposed framework is composed of modified networks for extracting the most correlated intermediate representations from off-pose thermal and frontal visible face imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss function comprised of cross-spectrum and pose-correction losses. We demonstrate efficacy and advantages of the proposed method by evaluating on three thermal-visible datasets: ARL Visible-to-Thermal Face, ARL Multimodal Face, and Tufts Face. Although DPIF focuses on learning to match off-pose thermal to frontal visible faces, we also show that DPIF enhances performance when matching frontal thermal face images to frontal visible face images.

Visual imagery of faces vs. places involves different functional connectivity patterns through an extended brain network including occipital, parietal and frontal areas

Visual imagery of faces vs. places involves different functional connectivity patterns through an extended brain network including occipital, parietal and frontal areas

Face recognition from research brain PET: An unexpected PET problem

It is well known that de-identified research brain images from MRI and CT can potentially be re-identified using face recognition; however, this has not been examined for PET images. We generated face reconstruction images of 182 volunteers using amyloid, tau, and FDG PET scans, and we measured how accurately commercial face recognition software (Microsoft Azure's Face API) automatically matched them with the individual participants’ face photographs. We then compared this accuracy with the same experiments using participants’ CT and MRI. Face reconstructions from PET images from PET/CT scanners were correctly matched at rates of 42% (FDG), 35% (tau), and 32% (amyloid), while CT were matched at 78% and MRI at 97-98%. We propose that these recognition rates are high enough that research studies should consider using face de-identification (“de-facing”) software on PET images, in addition to CT and structural MRI, before data sharing. We also updated our mri_reface de-identification software with extended functionality to replace face imagery in PET and CT images. Rates of face recognition on de-faced images were reduced to 0-4% for PET, 5% for CT, and 8% for MRI. We measured the effects of de-facing on regional amyloid PET measurements from two different measurement pipelines (PETSurfer/FreeSurfer 6.0, and one in-house method based on SPM12 and ANTs), and these effects were small: ICC values between de-faced and original images were > 0.98, biases were <2%, and median relative errors were < 2%. Effects on global amyloid PET SUVR measurements were even smaller: ICC values were 1.00, biases were <0.5%, and median relative errors were also <0.5%.

The connectional anatomy of visual mental imagery: evidence from a patient with left occipito-temporal damage.

Most of us can use our "mind's eye" to mentally visualize things that are not in our direct line of sight, an ability known as visual mental imagery. Extensive left temporal damage can impair patients' visual mental imagery experience, but the critical locus of lesion is unknown. Our recent meta-analysis of 27 fMRI studies of visual mental imagery highlighted a well-delimited region in the left lateral midfusiform gyrus, which was consistently activated during visual mental imagery, and which we called the Fusiform Imagery Node (FIN). Here, we describe the connectional anatomy of FIN in neurotypical participants and in RDS, a right-handed patient with an extensive occipito-temporal stroke in the left hemisphere. The stroke provoked right homonymous hemianopia, alexia without agraphia, and color anomia. Despite these deficits, RDS had normal subjective experience of visual mental imagery and reasonably preserved behavioral performance on tests of visual mental imagery of object shape, object color, letters, faces, and spatial relationships. We found that the FIN was spared by the lesion. We then assessed the connectional anatomy of the FIN in the MNI space and in the patient's native space, by visualizing the fibers of the inferior longitudinal fasciculus (ILF) and of the arcuate fasciculus (AF) passing through the FIN. In both spaces, the ILF connected the FIN with the anterior temporal lobe, and the AF linked it with frontal regions. Our evidence is consistent with the hypothesis that the FIN is a node of a brain network dedicated to voluntary visual mental imagery. The FIN could act as a bridge between visual information and semantic knowledge processed in the anterior temporal lobe and in the language circuits.

Charlotte-ThermalFace: A Fully Annotated Thermal Infrared Face Dataset with Various Environmental Conditions and Distances

Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects’ distance from the camera, the ambient temperature variation, and facial landmarks’ localization. We address these gaps by presenting a new facial thermography dataset.This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at https://github.com/TeCSAR-UNCC/UNCC-ThermalFace.

Research on High-Resolution Face Image Inpainting Method Based on StyleGAN

In face image recognition and other related applications, incomplete facial imagery due to obscuring factors during acquisition represents an issue that requires solving. Aimed at tackling this issue, the research surrounding face image completion has become an important topic in the field of image processing. Face image completion methods require the capability of capturing the semantics of facial expression. A deep learning network has been widely shown to bear this ability. However, for high-resolution face image completion, the network training of high-resolution image inpainting is difficult to converge, thus rendering high-resolution face image completion a difficult problem. Based on the study of the deep learning model of high-resolution face image generation, this paper proposes a high-resolution face inpainting method. First, our method extracts the latent vector of the face image to be repaired through ResNet, then inputs the latent vector to the pre-trained StyleGAN model to generate the face image. Next, it calculates the loss between the known part of the face image to be repaired and the corresponding part of the generated face imagery. Afterward, the latent vector is cut to generate a new face image iteratively until the number of iterations is reached. Finally, the Poisson fusion method is employed to process the last generated face image and the face image to be repaired in order to eliminate the difference in boundary color information of the repaired image. Through the comparison and analysis between two classical face completion methods in recent years on the CelebA-HQ data set, we discovered our method can achieve better completion results of 256*256 resolution face image completion. For 1024*1024 resolution face image restoration, we have also conducted a large number of experiments, which prove the effectiveness of our method. Our method can obtain a variety of repair results by editing the latent vector. In addition, our method can be successfully applied to face image editing, face image watermark clearing and other applications without the network training process of different masks in these applications.

Comparative Study on Effect of Proprioceptive Neuromuscular Facilitation and Facial Motor Imagery Techniques on Bell’s Palsy

Background Bellrsquos palsy BP was effectively treated with few physiotherapy approaches exercises and electrical stimulation. Studies have identified that Proprioceptive Neuromuscular Facilitation PNF Facial Motor Imagery FMI can manage disabilities of BP better. However no trials have compared the effect of PNF and FMI on BP. The objective of the study is to analyze and compare PNF and FMI in the recovery of impairment disability and facial nerve function in patients with BP.Methodology Forty-two patients with Bellrsquos palsy HB IV were recruited. The patients were divided into two groups. Group A received PNF and group B received FMI techniques and all the patients were treated with facial expression exercises and electrical stimulation for five days in a week for four weeks. The outcome measures like Facial Disability Index FDI Facial-Clinimetric Evaluation FaCE and House-Brackmann HB scale were implemented. Pre-and post intervention scores were recorded respectively.Results The analysis of the scores of HB FDI FaCE scales showed progressive changes in both the groups with 95 confidence interval plt0.001. PNF group exhibited greater recovery in comparison with motor imagery group in HB scale 2.24 to 2.71CI vs 1.29 to 1.76 CI respectively reduction in disability according to FDI physical sub scale 50.70 to53.10 CI vs 32.41 to 44.06 respectively and social sub-scale 37.11 to 41.74 vs 13.76 to 27.66 respectively scores and improved quality of life in terms of FaCE 32.08 to 37.44 vs17.47 to 25.39 respectively scores.Conclusion Both PNF and FMI are efficacious in minimizing the facial disability improving facial nerve function and enhancing the quality of life in patients with BP. The results of the statistical analysis showed that PNF increased effectiveness in the management of Bells palsy in comparison with FMI.