Face Images Research Articles

Distributed network flows generate localized category selectivity in human visual cortex.

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating - in a highly empirically constrained manner - the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region's unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain's intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses.

Poliface: A Multi-pose Synchronous Imaging System

To enhance the accuracy of face recognition technology in real-world scenarios, it is necessary to train deep learning models on datasets that contain a large number of labeled human face images under multiple poses, lighting, and accessory variations. In this paper, we introduce a novel acquisition system named the Poliface. This system can capture multiple high-resolution images simultaneously around the human head. We designed this system with a well-built aluminum structure, control electronic circuits, and high-performing in-house software. The results demonstrate the precise operation and exceptional stability of this system. Using this Poliface system, we have collected over 6 million photos, which can be used to train and evaluate facial recognition models, and exploited for three-dimensional (3D) virtual face reconstruction.

Accurate Age And Gender Prediction Using Hybrid CNN-LSTM Model From Face Images

Accurate Age And Gender Prediction Using Hybrid CNN-LSTM Model From Face Images

A method for measuring hairline length and discriminating hairline recession grades based on the BiSeNet model

Abstract Hair plays an important role in a person’s appearance. According to a survey by the World Health Organization, approximately 70% of adults have scalp and hair prob lems. Doctors currently make hairline recession diagnoses based on hair loss criteria, but this approach is subjective. This paper proposes a novel method for objectively assessing hairline recession grades. First, the Bilateral Segmentation Network model (BiSeNet) is utilized to obtain a facial segmentation image. Second, this paper utilizes the connected components method to improve the facial segmentation results. Next, the labeling key points method is used to extract part of the features of the eyebrow and facial region and calculate the related values. Finally, the judgment of hairline length and hairline recession grade is realized by combining these features with camera calibration. In this paper, front-face images of 50 volunteers were collected for hair line recession grade determination. The judgment results of expert doctors on hairline length and hairline recession grade were compared with the judgment results of this method. The results showed a 1.3 cm difference in the average length of the hairline and about 80% similarity in hairline recession grade judgments. In conclusion, using machine vision methods to measure the height of the hairline provides objective and repeatable results.

Tradeoffs between privacy and screening efficiency for air passengers – Implications for tourism supply chain management

ABSTRACT This study employs the stated preference method to explore airline passengers’ choices during security screening and examines their trade-offs between privacy and efficiency. We compare two Asian economic hub cities (i.e. Hong Kong and Shanghai), with 500 business and leisure travellers sampled from each city. A generalised mixed multinomial logit model is estimated. The results suggest that travellers display varied levels of privacy concerns, including biometrics, biographical data, identifiers, and behavioural information. The participants from Shanghai expressed a willingness to go through fingerprint scanning and to provide facial images. The travellers in Hong Kong displayed notable reluctance to receive iris scans and disclose political and religious affiliations, and they were particularly hesitant about sharing medication records. Despite some differences between the two markets, both groups strongly opposed smartphone tracking, including access to search history and GPS location. In general, business (vs. leisure) travellers, men (vs. women), individuals with higher education levels, and those with higher personal monthly incomes strongly preferred fast-track schemes that utilised personal data. Our findings highlight the possibility of making more effective transitions to high-tech screening methods that meet the changing expectations of travellers if total screening time can be significantly decreased.

Face Image Recognition by using some Dimension Reduction Algorithms and Logistic Regression

Face recognition is a common technique for artificial intelligence and image processing in which machine-learning algorithms automatically recognize a person's face through a huge image collection or even from a live video since there is difficulty in detecting data when using different shots for the same person in a facial recognition system, it takes time to determine person's membership. Furthermore, when working with high-dimensional data, where the 2D-pixel values of facial images, can be computationally challenging, the face recognition problem gets harder, so the current research compares the dimensionality reduction techniques like the principal component analysis algorithm PCA, an unsupervised learning algorithm that reduces dimensions by moving them from a high-dimensional area to a lower area without losing important information, and the two-dimensional principal component analysis algorithm 2DPCA to determine which technique is most effective for the process of detecting facial images, and this method will be compared with the logistic regression which is a statistical model for face recognition that has been used for classification tasks. Two different sets of data were used, the first group represented the ORL database, which was made up of 40 individuals, while the second group represented the real data, which was made up of 100 individuals from AL-Mamoon College, the results showed that the logistic regression model achieved the lowest MSE for ORL dataset and real data by achieving (0.0024414, 0.091936) respectively, followed by 2DPCA and PCA and all three techniques reached the highest accuracy rate of 100% for facial image recognition on real data. Paper type: Research paper

Retraction Note: Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors

Retraction Note: Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors

Design of Facial Expressions Recognition for Academic Presence By Using Backpropagation Artificial Neural Networks Based on Principal Components Analysis

There are 4,004 universities in Indonesia where each university needs data to find out student activities, one of which is through attendance. Some universities in Indonesia still use manual attendance systems and attendance systems through the website. This system has several obstacles that require solutions. Therefore, a more concise system is needed to assist students in filling in the attendance. This research aims to make a design to make academic presence for students by using neural network. There are many methods that can be used to create this system including using Principal Component Analysis (PCA) based on Backlpropagation Neural Network (BNN) because it can help the system perform faster and more accurately without losing important information. After carrying out a series of steps of algorithm designed for student attendance, we get the recognition of facial image expressions by using ANN Backpropagation and recognition of facial image expression with PCA.

Micro-Coring: A Novel Approach to Perioral Rejuvenation.

A long philtrum and poor perioral skin quality are stigmata of the aging face. Micro-Coring is a novel technology that allows for scarless skin removal. In this study we aimed to determine whether micro-coring can shorten the philtrum and improve perioral skin quality. A retrospective cohort study was performed on patients who underwent facelift with perioral micro-coring and age- and BMI-matched controls who underwent facelift alone. Preoperative and postoperative 3-dimensional facial imaging was performed. Standard perioral distances and percentage of change were calculated. Perioral skin quality was evaluated by blinded raters with the Scientific Assessment Scale of Skin Quality (SASSQ) and Global Aesthetic Improvement Scale (GAIS). Thirteen patients and 13 controls were included, with a mean follow-up of 8.9 months (range 3.0-21.5). Patients had significantly shorter mean philtrum length postoperatively compared to preoperatively, with an average decrease of 6.18% (±2.25%; P < .05). Controls did not experience significant changes in philtrum length (P > .05). There were no significant changes in other perioral measurements. Perioral skin elasticity and wrinkles significantly improved in patients compared to controls and patients had significantly greater GAIS scores (P < .05). Micro-Coring can achieve perioral rejuvenation through measurable shortening of the philtrum and observable improvement in skin quality. Nonsurgical techniques continue to find new ways to achieve aesthetic goals without significant recovery or scarring and offer value to patients and clinicians.

Design of a Safe Security System Based on Internet of Things Using Face and Fingerprint Detection

Traditional safe security systems usually use manual keys and a combination of numbers or passwords to open the safe. This system has several disadvantages such as being easy to break into, cumbersome, and the owner easily loses the key, even forgets the password needed to open the safe, which causes the safe to be unable to open. This research develops an Internet of Things (IoT)-based safe security system that uses two security options to open the safe, namely face detection and fingerprint authentication to increase security against unauthorized access with a prototype method. The system uses the ESP32-CAM to capture facial images and send them to the Telegram app for manual verification by the owner, while the fingerprint sensor ensures only registered users can open the safe. Arduino Uno serves as the main microcontroller to manage the integration between components such as ESP32-CAM, fingerprint sensor, relay, solenoid lock, LCD, and buzzer. The test results show that this system is effective in providing security to the safe through notifications, although it still relies on manual verification of faces via Telegram and requires a stable internet connection, and fingerprints that have been registered are successfully implemented. Further development is recommended to automate face recognition and improve the overall performance of the system.

Styled and characteristic Peking opera facial makeup synthesis with co-training and transfer conditional styleGAN2

Against the backdrop of the deep integration of culture and technology, research and practice in digitization of intangible cultural heritage has continued to deepen. However, due to the lack of data and training, it is still very difficult to apply artificial intelligence to the field of cultural heritage protection. This article integrates image generation technology into the digital protection of Peking opera facial makeup, using a self-built Peking opera facial makeup dataset. Based on the StyleGAN2 network, we propose a style generative cooperative training network Co-StyleGAN2, which integrates the adaptive data augmentation (ADA) to alleviate the problem of discriminator overfitting and introduces the idea of cooperative training to stabilize the training process. We design a Peking opera facial makeup image transform conditional generation network TC-StyleGAN2 which is transferred from unconditional generation network. The weights of the unconditional pre-training model are fixed, and an adaptive filtering modulation module is added to modulate the category parameters to complete the conversion from unconditional to conditional StyleGAN2 to deal with the training difficulty of conditional GANs on limited data, which suffer from severe mode collapse. The experimental results show that the proposed training strategy is better than the comparison algorithms, and the image generation quality and diversity have been improved.

Pyramid quaternion discrete cosine transform based ConvNet for cancelable face recognition

The current face scanning era can quickly and conveniently attain identity authentication, but face images imply sensitive information simultaneously. Under such context, we introduce a novel cancelable face recognition methodology by using quaternion transform based convolutional network. Firstly, face images in different modalities (e.g., RGB and depth or near-infrared) are encoded into full quaternion matrix for synchronous processing. Based on the designed multiresolution quaternion singular value decomposition, we can obtain pyramid representation. Then they are transformed through random projection for making the process noninvertible. Even if the feature template is compromised, a new one can be generated. Subsequently, a three-stream convolutional network is developed to learn features, where predefined filters are stemmed from quaternion two-dimensional discrete cosine transform basis. Extensive experiments on the TIII-D, NVIE and CASIA datasets have demonstrated that the proposed method obtains competitive performance, also satisfies redistributable and irreversible.

A comparative analysis of face and object perception in 2D laboratory and virtual reality settings: insights from induced oscillatory responses.

In psychophysiological research, the use of Virtual Reality (VR) for stimulus presentation allows for the investigation of how perceptual processing adapts to varying degrees of realism. Previous time-domain studies have shown that perceptual processing involves modality-specific neural mechanisms, as evidenced by distinct stimulus-locked components. Analyzing induced oscillations across different frequency bands can provide further insights into neural processes that are not strictly phase-locked to stimulus onset. This study uses a simple perceptual paradigm presenting images of faces and cars on both a standard 2D monitor and in an immersive VR environment. To investigate potential modality-dependent differences in attention, cognitive load, and task-related post-movement processing, the induced alpha, theta and beta band responses are compared between the two modalities. No evidence was found for differences in stimulus-dependent attention or task-related post-movement processing between the 2D conditions and the realistic virtual conditions in electrode space, as posterior alpha suppression and re-synchronization of centro-parietal beta did not differ between conditions. However, source analysis revealed differences in the attention networks engaged during 2D and 3D perception. Midfrontal theta was significantly stronger in laboratory conditions, indicating higher cognitive load than in the VR environment. Exploratory analysis of posterior theta showed stronger responses in VR, possibly reflecting the processing of depth information provided only by the 3D material. In addition, the theta response seems to be generated by distinct neuronal sources under realistic virtual conditions indicating enhanced involvement of semantic information processing and social cognition.

Deep Learning Approaches for Gender Classification from Facial Images

Gender recognition on the facial level is considered one of the most important technologies that finds use in such fields as a personalized marketing plan, safe systems of authentication, and effective human-computer interfaces. However, it has the following challenges; variation of lighting, facial movement, and ethnic/age face images. AI and DL has been improving on the effectiveness, flexibility, and speed of the gender classification system. AI enables complex and automatic feature learning in Data, while DL is tailored for handle variants in vision-based data. In this paper, we evaluated several architectures including Efficient Net_B2, ResNet50, ResNet18, and Lightning whilst determining the performance of the architectures in gender classification tasks. Self-assessment criteria included accuracy, precision, recall, and the F1-score. As for the performance, we found that ResNet18 had the highest scores on all the metrics, with the validation accuracy of above 98%, closely accompanied by the ResNet50 that, although it performed well as well, needed more epochs for convergence. The implications of this study for the development of future work in the gender classification technology include the discovery of ethnical, dependable, and effective techniques. Through the consideration of the state of the art and case studies, stakeholders can optimise the efficacy and the accountability of such systems, and thus support societal gains as a result of the improvement in technology.

Investigating Digital Illiterate Classification Techniques Based on DeepFace Technology

This paper presents an algorithm to identify digital illiterates by analyzing age and emotions from facial recognition. In this paper, digital illiterates refer to people who struggle in using digital devices. The study assumed that older individuals who display surprised or angry facial expressions while using digital devices are more likely to be identified as digitally illiterate. For age detection, the study used MTCNN (Multi-task Cascaded Convolutional Networks), and for emotion detection, it employed the VGG-Face model. MTCNN detects facial features and landmarks to preprocess images and distinguish facial characteristics. The VGG-Face model uses convolution operations to analyze facial images and classify emotional states. The dataset used in the study consisted of 3,000 facial images collected from the internet. The research team categorized the images into faces of individuals aged over 50, angry expressions, and surprised expressions. The dataset included 411 individuals (13.7%) aged over 50, 163 individuals (5.4%) with angry expressions, and 145 individuals (4.8%) with surprised expressions. Accuracy was estimated by comparing the results from the DeepFace algorithm with those from the research team’s classifications. The DeepFace algorithm achieved 95.77% accuracy in detecting individuals aged over 50, 83.45% accuracy for surprise, and 76.07% for anger. The results demonstrate that it is possible to identify digital illiterates based on their age and emotional expressions and could enable the development of personalized services to directly or indirectly support digital illiterates, potentially improving digital accessibility.

REGRESSION AND CLASSIFICATION MODELS FOR HUMAN AGE PREDICTION

This study aims to enhance automated age prediction from facial images, a task with significant potential in security, law enforcement, and Human-Computer Interaction (HCI). While age estimation has seen progress, it remains a challenging problem due to the diverse factors influencing facial aging, such as genetics, environment, lifestyle, and facial expressions. These variations result in individuals of the same chronological age looking markedly different. Most existing age estimation methods rely on computationally intensive pre-trained models, often treated as "black boxes" with predefined input sizes and limited flexibility. To address these limitations, we propose using Convolutional Neural Networks (CNNs) for age prediction. Our approach combines classification and regression techniques to predict age more accurately. We applied our model to publicly available datasets, including FGNET, Adience, APPA-REAL, UTKFace, and All-Age-Face, encompassing images from constrained and unconstrained environments. The proposed CNN model was evaluated against existing pre-trained models, demonstrating comparable performance in age prediction tasks. Both classification and regression results underscored the model's accuracy, offering additional benefits in reduced computational complexity, increased flexibility, and adaptability. This study introduces a CNN-based approach as a viable alternative to pre-trained models for automated age prediction. It offers competitive accuracy while addressing critical limitations of current models, such as computational demands and lack of flexibility, thus contributing a more efficient solution for age estimation tasks in various real-world applications.

Characteristics of posterior precortical vitreous pockets visualized by swept-source OCT and en face OCT

To determine the characteristics of posterior precortical vitreous pockets (PPVPs) and to observe vitreous changes in myopic eyes by swept-source optical coherence tomography (SS-OCT) and en face imaging in a cohort of the Chinese Han population. This was a cross-sectional study. Volunteers (235 participants, 374 eyes) received an SS-OCT examination. The heights and widths of the PPVPs were measured by SS-OCT. The area of the PPVPs was measured on en face images. The relationships between PPVP size and sex, age, axial length (AL) and spherical equivalent (SE) were evaluated. The mean width and height were 6711.64 ± 1241.87 μm and 662.47 ± 326.39 μm, respectively. The area of the PPVPs was 30.296 ± 9.114 mm2. Boat-shaped, oval, and hook-shaped PPVPs were observed in 73.26%, 21.12%, and 5.62% of all eyes, respectively; 73.53% of all PPVPs had channels communicating with Cloquet’s tubes. There was a significant difference in the PPVP width among the participates over and under 50 years old (t = -2.508, P = 0.031). Age had a positive correlation with the PPVP width (r = 0.53, P = 0.001). The PPVP height showed significant differences among the different myopia groups (F = 3.618, P = 0.013). SE had a negative correlation with the PPVP height (r = -0.176, P = 0.001). However, there were no correlations between the AL and the width, height or area of the PPVPs (P = 0.117; P = 0.334; P = 0.057, respectively). Age and myopia affect the size of PPVPs. SS-OCT greatly facilitates visualization of the complex structure of the vitreous.

Face and face pareidolia in patients with temporal lobe epilepsy indicates different neural processing: an event-related potential study

BackgroundVisual perception of face images or face pareidolia can be evaluated with event-related potentials (ERP) for healthy subjects and patients with neurological conditions. In this study, we aimed to analyse event-related potential components such as P100, N100, N170, and vertex-positive potential (VPP) in response to face pareidolia perception in temporal lobe epilepsy (TLE) patients.MethodsERPs were recorded during the pareidolia test. Waveforms were analzyed and current source density (CSD) maps were generated.ResultsCSD profiles were shown to be interpretable when face and face pareidolia conditions. N100, P100, and N170 components showed larger amplitudes and longer latency in epilepsy patients in response to face pareidolia stimuli compared to real face images. However, the N170 component latency did not differ significantly between epilepsy patients and healthy participants, while the larger amplitude and longer latency of N100 and P100 responses were evoked in healthy patients.ConclusionsOur results indicate a difference in the neural mechanisms of processing real face information and pareidolia face-like information in TLE patients.

FaceDefend: Copyright Protection to Prevent Face Embezzle

With the rapid evolution of deep learning and the advent of artificial intelligence, the metaverse has emerged as a significant technology. Within the metaverse, diverse elements such as rich applications and realistic digital avatars provide users with immersive experiences, but it poses a series of security problems. Current research predominantly focuses on the data storage and transmission processes from the perspective of blockchain and the Internet of Things to achieve the protection of the metaverse. However, there exists a gap in security research on the digital avatar generation process. Given that digital avatars are the primary entities engaging in social activities within the metaverse and are crafted based on real face images, the virtual character can be generated easily by stealing the user’s face image and controlled to interact with others. In order to deal with the above problems, we propose a novel method to prevent the misuse of faces, which maintains the security of the metaverse by protecting facial data and thus preventing its misuse. We explore the common architecture of generative models and propose a defense method based on copyright protection to prevent face embezzling. Firstly, we utilize the copyright protection module to obtain copyright protection information. Secondly, we utilized the defense control module to ensure the representation of the protected images occurs errors in the latent space of the generation model. Therefore the subsequent generation task output fails, which effectively protects the face data and prevents the generation of digital avatars. Furthermore, the results on public datasets and across multiple generative models present unnatural outputs, indicating the excellence of our defense and transfer capabilities.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.