Face Images Research Articles

Experimental Manipulation of the Bilateral Posterior Parietal Cortex Strengthens Associative Memory in Healthy Participants: A Continuous Theta-burst Stimulation.

To test whether targeting left and right posterior parietal cortex (PPC) with continuous theta-burst stimulation (cTBS) in healthy adults would strengthen associative memory (AM) performance. This study consisted of two experiments (a behavioral experiment and a formal experiment during each of the two experimental sessions). In Experiment 1, 18 adults (one male, age = 22.83 ± 3.92 years) were included in the behavioral phase and 18 adults (seven males, age = 40.11 ± 12.27 years) in the stimulation phase. There were 120 neutral facial images paired with 120 two-character nouns and then divided into six test versions (10 male faces and 10 female faces paired with 20 different nouns were considered as one version). In the behavioral experiment, participants were tested by six-version tests to assess memory materials, and in the formal experiment, participants' face-word AM performance was measured by certified tests based on a cued recall paradigm. Furthermore, 30 adults (seven males, age = 20.97 ± 1.85 years) and 15 adults (five males, age = 22.27 ± 1.29 years) participated in Experiment 2, respectively. Stimuli and procedure were the same as in Experiment 1, but the AM test was based on a forced-choice paradigm. Experiment 1 did not yield anticipated outcomes; Experiment 2 showed that cTBS of left and right PPC strengthened the AM performance compared with the control condition. In conclusion, cTBS to left and right PPC improved AM in healthy adults, which provided further experimental evidence for strengthening AM by cTBS.

Determinants of shared and idiosyncratic contributions to judgments of faces.

Recent work has shown that the idiosyncrasies of the observer can contribute more to the variance of social judgments of faces than the features of the faces. However, it is unclear what conditions determine the relative contributions of shared and idiosyncratic variance. Here, we examine two conditions: type of judgment and diversity of face stimuli. First, we show that for simpler, directly observable judgments that are consistent across observers (e.g., masculinity) shared exceeds idiosyncratic variance, whereas for more complex and less directly observable judgments (e.g., trustworthiness), idiosyncratic exceeds shared variance. Second, we show that judgments of more diverse face images increase the amount of shared variance. Finally, using machine-learning methods, we examine how stimulus (e.g., incidental emotion resemblance, skin luminosity) and observer variables (e.g., race, age) contribute to shared and idiosyncratic variance of judgments. Overall, our results indicate that an observer's age is the most consistent and best predictor of idiosyncratic variance contributions to face judgments measured in the current research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A modified deep learning method for Alzheimer’s disease detection based on the facial submicroscopic features in mice

Alzheimer’s disease (AD) is a chronic disease among people aged 65 and older. As the aging population continues to grow at a rapid pace, AD has emerged as a pressing public health issue globally. Early detection of the disease is important, because increasing evidence has illustrated that early diagnosis holds the key to effective treatment of AD. In this work, we developed and refined a multi-layer cyclic Residual convolutional neural network model, specifically tailored to identify AD-related submicroscopic characteristics in the facial images of mice. Our experiments involved classifying the mice into two distinct groups: a normal control group and an AD group. Compared with the other deep learning models, the proposed model achieved a better detection performance in the dataset of the mouse experiment. The accuracy, sensitivity, specificity and precision for AD identification with our proposed model were as high as 99.78%, 100%, 99.65% and 99.44%, respectively. Moreover, the heat maps of AD correlation in the facial images of the mice were acquired with the class activation mapping algorithm. It was proven that the facial images contained AD-related submicroscopic features. Consequently, through our mouse experiments, we validated the feasibility and accuracy of utilizing a facial image-based deep learning model for AD identification. Therefore, the present study suggests the potential of using facial images for AD detection in humans through deep learning-based methods.

Enhancing Biometric Security with Bimodal Deep Learning and Feature-level Fusion of Facial and Voice Data

Recent research in biometric technologies underscores the benefits of multimodal systems that use multiple traits to enhance security by complicating the replication of samples from genuine users. To address this, we present a bimodal deep learning network (BDLN or BNet) that integrates facial and voice modalities. Voice features are extracted using the SincNet architecture, and facial image features are obtained from convolutional layers. Proposed network fuses these feature vectors using either averaging or concatenation methods. A dense connected layer then processes the combined vector to produce a dual-modal vector that encapsulates distinctive user features. This dual-modal vector, processed through a softmax activation function and another dense connected layer, is used for identification. The presented system achieved an identification accuracy of 99% and a low equal error rate (EER) of 0.13% for verification. These results, derived from the VidTimit and BIOMEX-DB datasets, highlight the effectiveness of the proposed bimodal approach in improving biometric security.

P1 evoked by facial expression images is enhanced in Parkinson’s disease patients with depressive symptoms

IntroductionDepressive symptoms are most common non-motor symptoms in Parkinson’s disease (PD), which is often overlooked due to absence of rapid and objective diagnostic biomarkers. Electroencephalography (EEG)-based event-related potentials (ERPs) is commonly used to assess emotional processes. The aim of this study was to investigate changes in ERPs in PD patients exhibiting depressive symptoms and to provide a reliable biomarker for assisting in the diagnosis of PD with depressive symptoms.MethodsWe conducted a case–control study involving 30 PD patients with (dPD group) or without depressive symptoms (nPD group) and 13 age matched healthy controls (HC). We recorded EEG of the patients during the emotional picture stimulation task and analyzed the difference in the early ERPs potentials (P1, N170, early posterior negativity) and their correlation with the severity of symptoms in PD patients.ResultsOur results found that P1 amplitude in the occipital region of the dPD group in response to emotional faces was significantly higher than that of nPD and HC group, and it was positively correlated with severity of depressive symptoms in PD patients.ConclusionOur study shows that facial expression-induced enhancement of P1 amplitude can be utilized as a rapid and objective indicator to screen for depressive symptoms in PD.

ANALYSIS OF CANNY EDGE DETECTION METHOD FOR FACIAL RECOGNITION IN DIGITAL IMAGE PROCESSING

The document explores the application of the Canny Edge Detection method in facial recognition systems, specifically for identifying edge patterns in digital images. In the context of technological advancements, the focus is on enhancing data processing through efficient image analysis techniques. The research addresses how different edge detection methods, including Roberts, Prewitt, Sobel, and Canny, function, with the latter being highlighted for its superior ability to minimize error and deliver accurate edge detection results. The study outlines the development of a system designed to identify optimal edge detection parameters using the Canny method, focusing on facial images captured from the front. The system is limited to edge identification in such images, and performance is measured using a correlation coefficient. The process involves several technical steps, such as pre-processing the image (grayscale conversion and noise reduction) and using Gaussian filters and hysteresis thresholding to refine the detection. The research's ultimate aim is to optimize Canny's performance for identifying edges, contributing to advancements in facial recognition technology.

UK patients face imaging cancellations amid shortage of radioactive isotope.

UK patients face imaging cancellations amid shortage of radioactive isotope.

Audio-Driven Facial Animation with Deep Learning: A Survey

Audio-driven facial animation is a rapidly evolving field that aims to generate realistic facial expressions and lip movements synchronized with a given audio input. This survey provides a comprehensive review of deep learning techniques applied to audio-driven facial animation, with a focus on both audio-driven facial image animation and audio-driven facial mesh animation. These approaches employ deep learning to map audio inputs directly onto 3D facial meshes or 2D images, enabling the creation of highly realistic and synchronized animations. This survey also explores evaluation metrics, available datasets, and the challenges that remain, such as disentangling lip synchronization and emotions, generalization across speakers, and dataset limitations. Lastly, we discuss future directions, including multi-modal integration, personalized models, and facial attribute modification in animations, all of which are critical for the continued development and application of this technology.

Automated System for Credibility Diagnosis of Forged Facial Images Using Ensemble Deep Learning and Explainable AI

Automated System for Credibility Diagnosis of Forged Facial Images Using Ensemble Deep Learning and Explainable AI

New Insights on the Turin Shroud’s Body Image: Face Image at Different Wavelengths and its Double Superficiality

Various images of the face of the Turin Shroud Man, acquired at different wavelengths, from the near infrared region to the ultraviolet, have been studied and compared. A correlation as a function of the wavelength, between the penetration depth in the fabric of the incident light and the anatomic details visible on the images, is discussed based on the physical properties of linen threads and light. In addition, the backside of the Turin Shroud, in correspondence of the region of the face, has been examined, evidencing the possible presence of a second superficial image impressed on the backside. Notwithstanding the high level of scientific knowledge today reached, there is no known physical/chemical process which allows to explain all the properties of the Turin Shroud’s body image. According to our scientific knowledge, the corpse of a dead man cannot produce an energy capable to imprint on a linen cloth an image with all the microscopical and macroscopical properties discovered by analyzing the body image of the Turin Shroud, which has not yet been reproduced in any laboratory in the world. According to faith the corpse of a living Man can do it, during the resurrection, assuming a different state of matter. With this respect, the Turin Shroud can be considered as a two-thousand-old relic witnessing to the humanity, just in the age of science, the reasonableness of the faith in the resurrection.

Personalized Super Resolution with Face Prior

Face Super-Resolution (FSR) is a critical technology in computer vision that aims to reconstruct high-resolution facial images from low-resolution inputs. Despite recent advancements, current FSR methods struggle to accurately reconstruct personalized and detailed features. This paper proposes a novel FSR approach that addresses these challenges through a personalized feature extraction and fusion framework. Our method integrates a U-Net based downsampling mechanism to extract individual- specific features from high-resolution reference images, which are then fused with a pre-trained Generative Adversarial Network (GAN) for enhanced reconstruction. We introduce a comprehensive loss function that combines reconstruction, adversarial, facial component, and identity preservation losses to guide the learning process. Extensive experiments on the augmented FFHQ dataset demonstrate that our approach significantly improves the reconstruction of rich facial features, particularly for older individuals, outperforming existing state-of-the-art methods in both quantitative metrics and qualitative visual assessments.

Different Pathways of Skin Aging: Objective Instrumental Evaluation

Background/Objectives: Hypertrophic and atrophic skin aging represent two distinct phenotypes: hypertrophic aging is marked by deep wrinkles and a leathery texture, whereas atrophic aging is characterized by overall skin thinning, increased vascularity, and a higher risk of non-melanoma skin cancers. This study aims to elucidate the characteristics and differences between hypertrophic and atrophic facial aging subtypes using two non-invasive imaging devices: VISIA® and dynamic optical coherence tomography (D-OCT). Methods: We retrospectively evaluated patients who had presented to the outpatient dermatological clinic at Policlinico Umberto I hospital in Rome, Italy for a non-invasive facial imaging check-up. We included 40 patients aged 60–75 who were imaged with VISIA® and dynamic optical coherence tomography (D-OCT). Based on the number of UV spots and amount of red found on VISIA®, subjects were grouped into four subgroups (PIGM, RED, CONTROL, PIGM + RED), and trends among them were analyzed. Results: We found a strong correlation between VISIA® red area scores and D-OCT vascular density at 300 µm depth, confirming VISIA®’s effectiveness for assessing facial vascularity. Wrinkle count was highest in areas with UV spots, particularly in the PIGM and PIGM + RED groups. Conversely, low attenuation coefficients and dermal density were observed in regions with low UV spots but high red areas. Intermediate subgroups (CONTROL and PIGM + RED) displayed varying parameters. Conclusions: Non-invasive imaging devices are effective in evaluating facial aging and distinguishing between aging subtypes. This study identified two intermediate aging types in addition to the hypertrophic and atrophic subtypes.

Quantitative analysis of facial shape in children to support respirator design

The COVID-19 pandemic demonstrated the need for respiratory protection against airborne pathogens. Respirator options for children are limited, and existing designs do not consider differences in facial shape or size. We created a dataset of children's facial images from three cohorts, then used geometric morphometric analyses of dense and sparse facial landmark representations to quantify age, sex and ancestry-related variation in shape. We found facial shape and size in children vary significantly with age from ages 2 to 18, particularly in dimensions relevant to respirator design. Sex differences are small throughout most of the age range of our sample. Ancestry is associated with significant facial shape variation in dimensions that may affect respirator fit. We offer guidance on how to our results can be used for the appropriate design of devices such as respirators for pediatric populations. We also highlight the need to consider ancestry-related variation in facial morphology to promote equitable, inclusive products.

Frequency‐Aware Facial Image Shadow Removal through Skin Color and Texture Learning

AbstractExisting facial image shadow removal methods predominantly rely on pre‐extracted facial features. However, these methods often fail to capitalize on the full potential of these features, resorting to simplified utilization. Furthermore, they tend to overlook the importance of low‐frequency information during the extraction of prior features, which can be easily compromised by noises. In our work, we propose a frequency‐aware shadow removal network (FSRNet) for facial image shadow removal, which utilizes the skin color and texture information in the face to help recover illumination in shadow regions. Our FSRNet uses a frequency‐domain image decomposition network to extract the low‐frequency skin color map and high‐frequency texture map from the face images, and applies a color‐texture guided shadow removal network to produce final shadow removal result. Concretely, the designed fourier sparse attention block (FSABlock) can transform images from the spatial domain to the frequency domain and help the network focus on the key information. We also introduce a skin color fusion module (CFModule) and a texture fusion module (TFModule) to enhance the understanding and utilization of color and texture features, promoting high‐quality result without color distortion and detail blurring. Extensive experiments demonstrate the superiority of the proposed method. The code is available at https://github.com/laoxie521/FSRNet.

Spatially and Temporally Optimized Audio‐Driven Talking Face Generation

AbstractAudio‐driven talking face generation is essentially a cross‐modal mapping from audio to video frames. The main challenge lies in the intricate one‐to‐many mapping, which affects lip sync accuracy. And the loss of facial details during image reconstruction often results in visual artifacts in the generated video. To overcome these challenges, this paper proposes to enhance the quality of generated talking faces with a new spatio‐temporal consistency. Specifically, the temporal consistency is achieved through consecutive frames of the each phoneme, which form temporal modules that exhibit similar lip appearance changes. This allows for adaptive adjustment in the lip movement for accurate sync. The spatial consistency pertains to the uniform distribution of textures within local regions, which form spatial modules and regulate the texture distribution in the generator. This yields fine details in the reconstructed facial images. Extensive experiments show that our method can generate more natural talking faces than previous state‐of‐the‐art methods in both accurate lip sync and realistic facial details.

Disentangled Lifespan Synthesis via Transformer‐Based Nonlinear Regression

AbstractLifespan face age transformation aims to generate facial images that accurately depict an individual's appearance at different age stages. This task is highly challenging due to the need for reasonable changes in facial features while preserving identity characteristics. Existing methods tend to synthesize unsatisfactory results, such as entangled facial attributes and low identity preservation, especially when dealing with large age gaps. Furthermore, over‐manipulating the style vector may deviate it from the latent space and damage image quality. To address these issues, this paper introduces a novel nonlinear regression model‐Disentangled Lifespan face Aging (DL‐Aging) to achieve high‐quality age transformation images. Specifically, we propose an age modulation encoder to extract age‐related multi‐scale facial features as key and value, and use the reconstructed style vector of the image as the query. The multi‐head cross‐attention in the W+ space is utilized to update the query for aging image reconstruction iteratively. This nonlinear transformation enables the model to learn a more disentangled mode of transformation, which is crucial for alleviating facial attribute entanglement. Additionally, we introduce a W+ space age regularization term to prevent excessive manipulation of the style vector and ensure it remains within the W+ space during transformation, thereby improving generation quality and aging accuracy. Extensive qualitative and quantitative experiments demonstrate that the proposed DL‐Aging outperforms state‐of‐the‐art methods regarding aging accuracy, image quality, attribute disentanglement, and identity preservation, especially for large age gaps.

Skin feature point tracking using deep feature encodings

AbstractFacial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson’s disease. While traditional computer vision algorithms like Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK) have been benchmarks due to their efficiency and accuracy, they often struggle with challenges like affine transformations and changes in illumination. In response, we propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained upon. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labelled face and hand videos of small and large motion recorded in our lab. Our evaluation protocol is comprehensive, including quantification of errors in human annotation. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude the fact that they stem from the manual labelling based on a $$\chi ^2$$ χ 2 -test. With a mean error of 0.6–3.3 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one that did not diverge. We also compare our method with the latest state-of-the-art transformer for feature matching by Google—Omnimotion. Our results indicate that our method is superior at tracking different skin features under large motion conditions and that it creates better feature descriptors for tracking, matching, and image registration compared to both traditional algorithms and the latest Omnimotion.

A Two-Stream Method for Human Action Recognition Using Facial Action Cues

Human action recognition (HAR) is a critical area in computer vision with wide-ranging applications, including video surveillance, healthcare monitoring, and abnormal behavior detection. Current HAR methods predominantly rely on full-body data, which can limit their effectiveness in real-world scenarios where occlusion is common. In such situations, the face often remains visible, providing valuable cues for action recognition. This paper introduces Face in Action (FIA), a novel two-stream method that leverages facial action cues for robust action recognition under conditions of significant occlusion. FIA consists of an RGB stream and a landmark stream. The RGB stream processes facial image sequences using a fine-spatio-multitemporal (FSM) 3D convolution module, which employs smaller spatial receptive fields to capture detailed local facial movements and larger temporal receptive fields to model broader temporal dynamics. The landmark stream processes facial landmark sequences using a normalized temporal attention (NTA) module within an NTA-GCN block, enhancing the detection of key facial frames and improving overall recognition accuracy. We validate the effectiveness of FIA using the NTU RGB+D and NTU RGB+D 120 datasets, focusing on action categories related to medical conditions. Our experiments demonstrate that FIA significantly outperforms existing methods in scenarios with extensive occlusion, highlighting its potential for practical applications in surveillance and healthcare settings.

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.