Search Results (45)

Background/Objectives: Tension-type headache (TTH) is the most prevalent form of primary headache. The etiology of TTH is not yet fully understood, although it is associated with the presence of myofascial trigger points (MTPs) in cervical and facial muscles. Dry needling (DN) therapy has emerged as an effective and safe non-pharmacological option for pain relief, but there are a lack of systematic reviews focused on its specific characteristics in TTH. The aim of this paper is to examine the characteristics and methodologies of DN in managing TTH. Methods: A scoping review was conducted with inclusion criteria considering studies that evaluated DN interventions in adults with TTH, reporting target muscles, diagnostic criteria, and technical features. The search was performed using PubMed, Embase, Scopus, and the Web of Science, resulting in the selection of seven studies after a rigorous filtering and evaluation process. Results: The included studies, primarily randomized controlled trials, involved a total of 309 participants. The most frequently treated muscles were the temporalis and trapezius. Identification of MTPs was mainly performed through manual palpation, although diagnostic criteria varied. DN interventions differed in technique. All studies included indicated favorable outcomes with improvements in headache symptoms. No serious adverse effects were reported, suggesting that the technique is safe. However, heterogeneity in protocols and diagnostic criteria limits the comparability of results. Conclusions: The evidence supports the use of DN in key muscles such as the temporalis and trapezius for managing TTH, although the diversity in methodologies and diagnostic criteria highlights the need for standardization. The safety profile of the method is favorable, but further research is necessary to define optimal protocols and improve reproducibility. Implementing objective diagnostic criteria and uniform protocols will facilitate advances in clinical practice and future research, ultimately optimizing outcomes for patients with TTH. Full article

Facial occlusion represents a significant challenge in the domain of facial expression recognition (FER). The absence of feature information due to occlusion has been demonstrated to result in a reduction in recognition accuracy and model robustness. To address this challenge, a multi-angle feature extraction (MAFE) method is proposed in this paper, aiming to enhance the recognition accuracy under occlusion conditions by employing multi-scale global features, local fine-grained features, and important regional features. The MAFE approach involves three core modules: multi-feature extraction, regional detail feature fusion, and consistent feature recognition. In the multi-feature extraction module, PTIR-50 and Swin Transformer are used to extract global features and fine-grained features, and at the same time, the five key points of the face are combined to crop out the important regions from the global features. The Regional Bias Loss (RB-Loss) is then utilized to guide the model to focus on the key information regions. The subsequent Regional Detail Feature Fusion module combines fine-grained features with those from the important regions. This process enhances the expressiveness of the features. The Consistent Feature Recognition module proposes consistent feature loss (con-feature Loss) to ensure that global features and fused features guide each other, forcing the model to focus on more discriminative expression features. The experimental results demonstrate that MAFE attains 89.42% and 86.94% accuracies on the Occlusion-RAFDB and Occlusion-FERPlus datasets, thereby surpassing the existing methods. Accuracies of 92.11% and 90.15% are also obtained on the original RAF-DB and FERPlus datasets. Full article

Body posture dynamics have garnered significant attention in recent years due to their critical role in understanding the emotional states conveyed through human movements during social interactions. Emotions are typically expressed through facial expressions, voice, gait, posture, and overall body dynamics. Among these, body posture provides subtle yet essential cues about emotional states. However, predicting an individual’s gait and posture dynamics poses challenges, given the complexity of human body movement, which involves numerous degrees of freedom compared to facial expressions. Moreover, unlike static facial expressions, body dynamics are inherently fluid and continuously evolving. This paper presents an effective method for recognizing 17 micro-emotions by analyzing kinematic features from the GEMEP dataset using video-based motion capture. We specifically focus on upper body posture dynamics (skeleton points and angle), capturing movement patterns and their dynamic range over time. Our approach addresses the complexity of recognizing emotions from posture and gait by focusing on key elements of kinematic gesture analysis. The experimental results demonstrate the effectiveness of the proposed model, achieving a high accuracy rate of 91.48% for angle metric + DNN and 93.89% for distance + DNN on the GEMEP dataset using a deep neural network (DNN). These findings highlight the potential for our model to advance posture-based emotion recognition, particularly in applications where human body dynamics distance and angle are key indicators of emotional states. Full article

Facial landmarks, as direct and reliable biometric features, are widely utilized in various fields, including information security, public safety, virtual reality, and augmented reality. Facial landmarks, which are discrete key points on the face, preserve expression features and maintain the topological structure between facial organs. Fast and accurate facial landmark prediction is essential in solving computer vision problems involving facial analysis, particularly in occlusion scenarios. This research proposes a lightweight facial landmark prediction network for occluded faces using an improved depthwise separable convolutional neural network architecture. The model is trained using 30,000 images from the CelebA-HQ dataset. The model is then tested under different occlusion ratios, including 10–20%, 30–40%, 40–50%, and 50–60% random occlusion, as well as 25% center occlusion. Using 68 facial landmarks for occlusion prediction, the proposed method always achieved significant improvements. Experimental results show that the proposed lightweight facial landmark prediction method is 1.97 times faster than FAN* and 1.67 times faster than ESR*, while still achieving better prediction results with lower NMSE values across all tested occlusion ratios for both frontal and profile faces. Full article

Background: Skull base tumors can extend into the temporal bone and occasionally even be visible through the tympanic membrane (TM) if they grow into the middle ear cavity. The differential diagnosis of a skull base mass is extensive and ranges from non-tumorous lesions like cholesteatoma to benign tumors like schwannoma and to malignant lesions like metastatic cancer. Optical coherence tomography (OCT) is a noninvasive imaging technique that can image tissue with high resolution in three dimensions, including through structures such as the TM and bone. OCT angiography is also able to assess tissue vascularity. We hypothesized that OCT could help shrink the differential diagnosis in clinic on the day of initial presentation. Specifically, we thought that OCT angiography could help distinguish between highly vascular skull base tumors such as glomus jugulare and other less vascular tumors and middle ear pathologies such as cholesteatoma and schwannoma. Objectives: We sought to determine whether OCT can image through the TM in clinic to distinguish a normal ear from an ear with a mass behind the tympanic membrane. Furthermore, we sought to assess whether OCT angiography can detect vascularity in these masses to help inform the diagnosis. Methods: We designed and built a custom handheld OCT system that can be used like an otoscope in clinic. It is based off a 200 kHz swept-source laser with a center wavelength of 1310 nm and a bandwidth of 39 nm. It provides a 33.4 μm axial and 38 μm lateral resolution. Cross-sectional images of the middle ear space, including OCT angiography, were captured in an academic neurotology clinic. Patients with normal ear exams, glomus tumors, cholesteatomas, and facial nerve schwannoma were imaged. Results: OCT images revealed key structures within the middle ear space, including the TM, ossicles (malleus and incudostapedial joint), chorda tympani, and cochlear promontory. OCT also identified middle ear pathology (using pixel intensity ratio in the middle ear normalized to the TM) when compared with patients with normal ear exams (mean 0.082, n = 6), in all patients with a glomus tumor (mean 0.620, n = 6, p < 0.001), cholesteatoma (mean 0.153, n = 4, p < 0.01), and facial nerve schwannoma (0.573, n = 1). OCT angiography revealed significant vascularity within glomus tumors (mean 1.881, n = 3), but minimal vascularity was found in normal ears (mean 0.615, n = 3, p < 0.05) and ears with cholesteatoma (mean 0.709, n = 3, p < 0.01), as expected. Conclusions: OCT is able to image through the TM and detect middle ear masses. OCT angiography correctly assesses the vascularity within these masses. Thus, OCT permits the clinician to have additional point-of-care data that can help make the correct diagnosis. Full article

Face recognition technology, despite its widespread use in various applications, still faces challenges related to occlusions, pose variations, and expression changes. Three-dimensional face recognition with depth information, particularly using point cloud-based networks, has shown effectiveness in overcoming these challenges. However, due to the limited extent of extensive 3D facial data and the non-rigid nature of facial structures, extracting distinct facial representations directly from point clouds remains challenging. To address this, our research proposes two key approaches. Firstly, we introduce a learning framework guided by a small amount of real face data based on morphable models with Gaussian processes. This system uses a novel method for generating large-scale virtual face scans, addressing the scarcity of 3D data. Secondly, we present a dual-branch network that directly extracts non-rigid facial features from point clouds, using kernel point convolution (KPConv) as its foundation. A local neighborhood adaptive feature learning module is introduced and employs context sampling technology, hierarchically downsampling feature-sensitive points critical for deep transfer and aggregation of discriminative facial features, to enhance the extraction of discriminative facial features. Notably, our training strategy combines large-scale face scanning data with 967 real face data from the FRGC v2.0 subset, demonstrating the effectiveness of guiding with a small amount of real face data. Experiments on the FRGC v2.0 dataset and the Bosphorus dataset demonstrate the effectiveness and potential of our method. Full article

Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF angles are subjective when measured manually. To address this challenge, we propose a deep learning-based assisted examination framework for automatically measuring FMF angles on 2D ultrasound images. Firstly, we trained a deep learning network using 1549 fetal ultrasound images to achieve automatic and accurate segmentation of critical areas. Subsequently, a key point detection network was employed to predict the coordinates of the requisite points for calculating FMF angles. Finally, FMF angles were obtained through computational means. We employed Pearson correlation coefficients and Bland–Altman plots to assess the correlation and consistency between the model’s predictions and manual measurements. Notably, our method exhibited a mean absolute error of 2.354°, outperforming the typical standards of the junior expert. This indicates the high degree of accuracy and reliability achieved by our approach. Full article

General face recognition is currently one of the key technologies in the field of computer vision, and it has achieved tremendous success with the support of deep-learning technology. General face recognition models currently exhibit extremely high accuracy on some high-quality face datasets. However, their performance decreases in challenging environments, such as low-light scenes. To enhance the performance of face recognition models in low-light scenarios, we propose a face recognition approach based on feature decoupling and fusion (DeFFace). Our main idea is to extract facial-related features from images that are not influenced by illumination. First, we introduce a feature decoupling network (D-Net) to decouple the image into facial-related features and illumination-related features. By incorporating the illumination triplet loss optimized with unpaired identity IDs, we regulate illumination-related features to minimize the impact of lighting conditions on the face recognition system. However, the decoupled features are relatively coarse. Therefore, we introduce a feature fusion network (F-Net) to further extract the residual facial-related features from the illumination-related features and fuse them with the initial facial-related features. Finally, we introduce a lighting-facial correlation loss to reduce the correlation between the two decoupled features in the specific space. We demonstrate the effectiveness of our method on four real-world low-light datasets and three simulated low-light datasets. We retrain multiple general face recognition methods using our proposed low-light training sets to further validate the advanced performance of our method. Compared to general face recognition methods, our approach achieves an average improvement of more than 2.11 percentage points on low-light face datasets. In comparison with image enhancement-based solutions, our method shows an average improvement of around 16 percentage points on low-light datasets, and it also delivers an average improvement of approximately 5.67 percentage points when compared to illumination normalization-based methods. Full article

Background: Unilateral condylar hyperplasia (UCH) is characterized by slow progression and enlargement of the condyle, accompanied by elongation of the mandibular body, resulting in facial asymmetry, occlusal disharmony, and joint dysfunction. This condition can be defined as “active” or “inactive”: the active form is characterized by continuous growth and dynamic histologic changes, whereas the inactive form indicates that the growth process has stabilized. Since there are few microscopic studies on the inactive form, this study aims to investigate the histological features and expression of key proteins and bone markers in patients diagnosed with inactive UCH. Methods: A total of 15 biopsies from patients aged 28 to 36 years were examined by light microscopy and immunofluorescence for collagen I and II, metalloproteinases 2 (MMP-2) and 9 (MMP-9), receptor activator of nuclear factor- kappa B (RANK), and osteocalcin. Results: Our findings indicate that during inactive UCH, the ongoing process is not entirely stopped, with moderate expression of collagen, metalloproteinases, RANK, and osteocalcin, although no cartilage islands are detectable. Conclusions: The present study shows that even if these features are moderate when compared to active UCH and without cartilage islands, inactive UCH could be characterized by borderline features that could represent an important trigger-point to possible reactivation, or they could represent a long slow progression that is not “self-limited”. Full article

Convolutional neural networks have made significant progress in human Facial Expression Recognition (FER). However, they still face challenges in effectively focusing on and extracting facial features. Recent research has turned to attention mechanisms to address this issue, focusing primarily on local feature details rather than overall facial features. Building upon the classical Convolutional Block Attention Module (CBAM), this paper introduces a novel Parallel Hybrid Attention Model, termed PH-CBAM. This model employs split-channel attention to enhance the extraction of key features while maintaining a minimal parameter count. The proposed model enables the network to emphasize relevant details during expression classification. Heatmap analysis demonstrates that PH-CBAM effectively highlights key facial information. By employing a multimodal extraction approach in the initial image feature extraction phase, the network structure captures various facial features. The algorithm integrates a residual network and the MISH activation function to create a multi-feature extraction network, addressing issues such as gradient vanishing and negative gradient zero point in residual transmission. This enhances the retention of valuable information and facilitates information flow between key image details and target images. Evaluation on benchmark datasets FER2013, CK+, and Bigfer2013 yielded accuracies of 68.82%, 97.13%, and 72.31%, respectively. Comparison with mainstream network models on FER2013 and CK+ datasets demonstrates the efficiency of the PH-CBAM model, with comparable accuracy to current advanced models, showcasing its effectiveness in emotion detection. Full article

Background: Facial hypermelanosis is a major cosmetic issue that causes severe social embarrassment and psychological pain, particularly among Asians and dark-skinned individuals. Aim: This study assesses the safety and effectiveness of Q-switched 1064/532 nm nanosecond/picosecond lasers in removing benign hypermelanosis in dark-skinned individuals, evaluating the possible associated side effects. Material and methods: A total of 30 participants (80% females and 20% males) with Fitzpatrick skin types IV–V–VI who presented superficial benign hypermelanoses on the facial and décolleté area were enrolled. All patients underwent to one to two laser treatment sessions with a 1064/532 nm Q-switched laser system. Three months after the final laser session, results were assessed by comparing before- and after-treatment photos and using a quartile scale for lesion clearance (4-point Investigator Global Assessment scale). Results: All patients observed global improvements in their pigmented lesions: 53% of patients achieved excellent clearance, 30% of patients achieved good to moderate clearance, 10% of patients achieved slight clearance, and 7% of patients did not respond to the therapy. No serious adverse event occurred. Photos showed the clinical improvement achieved at 3 months follow-up. Conclusions: The Q-switched 1064/532 nm laser proved to be a key tool for treating benign hypermelanosis in all skin types, including dark-skinned persons. Full article

Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods. Full article

In this paper, I explore Kierkegaard’s views on envy as developed in A Literary Review, by confronting them with the capital vices tradition. I begin by developing a basic account of envy that serves as a point of reference throughout the paper. I then turn to the capital vices tradition, elaborating the concept of a capital vice, and discussing the views of Basil of Caesarea, Evagrius of Pontus, John Cassian, Gregory the Great, and Thomas Aquinas on envy’s viciousness. Subsequently, I discuss Kierkegaard’s treatment of envy in A Literary Review, exploring two of its key notions—‘the public’ and ‘leveling’—through a reading of L.P. Hartley’s novel Facial Justice (1960). In the final part of the paper, I show that the originality of Kierkegaard’s account of envy consists both in its character as a collective vice and its evaluative status as vicious yet valuable. Full article

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the ‘yawning’ and ‘no_yawning’ classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model. Full article

Background: Orthognathic surgery is a multidisciplinary surgery in which the aesthetic results have become increasingly important, and consequently, also the predictability of the surgical outcomes. In this paper, we analyzed the volumetric distribution of the lower two-thirds of the face, in patients operated by orthognathic surgery and selected for their attractiveness. Our goal was to analyze the aesthetic volumetric distribution for gender and to propose our operating philosophy, that a normative distribution of facial volumes could be used like a new 3D aesthetic guide in orthognathic planning. Methods: A group of 46 orthognathic patients (26 females, 20 males) with the best postoperative aesthetic score was selected by a jury of plastic surgeons, orthodontists, and journalists. The mean soft tissue volumes of the malar, maxillary, mandibular, and chin regions were analyzed. Results: Overall, we measured a mean female facial volume distribution of 38.7%, 29%, 27.6%, and 4.7%, respectively, in the malar, maxillary, mandibular, and chin regions, while in males, it was 37%, 26%, 30%, and 6%, respectively. Conclusions: In this paper, the expansion of facial volumes in orthognathic surgery is proposed as a key point for facial harmonization. Beauty could be scientifically interpreted as a balanced distribution of facial volumes, and the virtual study of this distribution can become an important part of the preoperative analysis, such as a “volumetric” 3D cephalometry, where the surgeon could use average values of aesthetic volumetric distribution as preoperative surgical references. Full article