Important Facial Features Research Articles

Facial Features of Hereditary Cancer Predisposition.

In the age of telehealth medicine, an individual's facial features may provide the only physical clues signaling the presence of a heritable cancer predisposition syndrome. These syndromes include APC-associated polyposis, Birt-Hogg-Dubé syndrome, CYLD cutaneous syndrome, hereditary leiomyomatosis and renal cell cancer, multiple endocrine neoplasia, neurofibromatosis type 1, Peutz-Jeghers syndrome, PTEN hamartoma tumor syndrome, and tuberous sclerosis complex 1 and 2, among others. Correctly identifying characteristic features is important for genetic and nongenetic specialists as early detection can enable prompt intervention, improving patient outcomes. Advancements in the availability of genetic testing allow patients and their relatives to have more information about their genetic risk profile than before. These changes in clinical pathways, combined with improvements in screening and risk-reducing treatment, highlight the need to outline the cutaneous and morphologic features of high-risk cancer syndromes for clinicians. In this review, we describe the important facial features of hereditary cancer predisposition, with emphasis on diagnosis, cutaneous and extracutaneous manifestations, and screening.

Fluid face but not gender: Enfacement illusion through digital face filters does not affect gender identity.

It has been shown that observing a face being touched or moving in synchrony with our own face increases self-identification with the former which might alter both cognitive and affective processes. The induction of this phenomenon, termed enfacement illusion, has often relied on laboratory tools that are unavailable to a large audience. However, digital face filters applications are nowadays regularly used and might provide an interesting tool to study similar mechanisms in a wider population. Digital filters are able to render our faces in real time while changing important facial features, for example, rendering them more masculine or feminine according to normative standards. Recent literature using full-body illusions has shown that participants' own gender identity shifts when embodying a different gendered avatar. Here we studied whether participants' filtered faces, observed while moving in synchrony with their own face, may induce an enfacement illusion and if so, modulate their gender identity. We collected data from 35 female and 33 male participants who observed a stereotypically gender mismatched version of themselves either moving synchronously or asynchronously with their own face on a screen. Our findings showed a successful induction of the enfacement illusion in the synchronous condition according to a questionnaire addressing the feelings of ownership, agency and perceived similarity. However, we found no evidence of gender identity being modulated, neither in explicit nor in implicit measures of gender identification. We discuss the distinction between full-body and facial processing and the relevance of studying widely accessible devices that may impact the sense of a bodily self and our cognition, emotion and behaviour.

Anthropometric Study of the External Ear Among Delta State University Students in Abraka, Nigeria

Context: The morphology of the ear auricle contributes to the important facial features, which influence the facial appearance of an individual. Aim: The aim of this inquiry was to present the anthropometric details of the ear auricle and lobe among Delta State University students in Abraka, Nigeria. Setting and Design: This enquiry adopted a descriptive cross-sectional study design and considered both male and female Delta State University Students in Abraka aged between 17 and 31 years. The sample size for this study was 384 (204 males and 180 females), and the simple random sampling technique was used. Data were obtained with the use of Vernier caliper (standard digital sliding Vernier caliper), a tape rule, data collection sheet, and pencil, which were used for measurement and recording. Ethical clearance/approval was secured from the Ethical Board of the Department of Human Anatomy and Cell Biology in the Delta State University in Abraka, Nigeria. The data obtained were analyzed with the Statistical Package for the Social Sciences (SPSS) version 20.0.0. Results: The left and right auricular width and height and lobular width and height were observed to be higher in males when compared with females. There was no significant gender difference in the right ear auricular height, left and right ear lobular height, and left and right ear lobular width (P > 0.05). However, there was a significant gender difference in the left auricular height and left auricular width (P < 0.05). Conclusion: This enquiry provides a template for designers and ergonomists in designing prostheses and manufacturing ear microphone for various age brackets as well as provides a guide for plastic surgeons in construction and determination of the exact auricular framework across various age brackets.

A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning

Numerous studies discuss the features that constitute facial attractiveness. In recent years, computational research has received attention because it can examine facial features without relying on prior research hypotheses. This approach uses many face stimuli and models the relationship between physical facial features and attractiveness using methods such as geometric morphometrics and deep learning. However, studies using each method have been conducted independently and have technical and data-related limitations. It is also difficult to identify the factors of actual attractiveness perception using only computational methods. In this study, we examined morphometric features important for attractiveness perception through geometric morphometrics and impression evaluation. Furthermore, we used deep learning to analyze important facial features comprehensively. The results showed that eye-related areas are essential in determining attractiveness and that different racial groups contribute differently to the impact of shape and skin information on attractiveness. The approach used in this study will contribute toward understanding facial attractiveness features that are universal and diverse, extending psychological findings and engineering applications.

Sheep Face Detection Based on an Improved RetinaFace Algorithm.

The accurate breeding of individual sheep has shown outstanding effectiveness in food quality tracing, prevention of fake insurance claims, etc., for which sheep identification is the key to guaranteeing its high performance. As a promising solution, sheep identification based on sheep face detection has shown potential effectiveness in recent studies. Unfortunately, the performance of sheep face detection has still been a challenge due to diverse background illumination, sheep face angles and scales, etc. In this paper, an effective and lightweight sheep face detection method based on an improved RetinaFace algorithm is proposed. In order to achieve an accurate and real-time detection of sheep faces on actual sheep farms, the original RetinaFace algorithm is improved in two main aspects. Firstly, to accelerate the speed of multi-scale sheep face feature extraction, an improved MobileNetV3-large with a switchable atrous convolution is optimally used as the backbone network of the proposed algorithm. Secondly, the channel and spatial attention modules are added into the original detector module to highlight important facial features of the sheep. This helps obtain more discriminative sheep face features to mitigate against the challenges of diverse face angles and scale in sheep. The experimental results on our collected real-world scenarios have shown that the proposed method outperforms others with an F1score of 95.25%, an average precision of 96.00%, a model size of 13.20 M, an average processing time of 26.83 ms, and a parameter of 3.20 M.

Real-Time Occluded Face Identification Using Deep Learning

One of the most difficult aspects of face identification is face occlusion. Face occlusion is when anything is placed over the face, for example, a mask. Masks occlude multiple important facial features, like the chin, lips, nose, and facial edges. Face identification becomes challenging when important facial features are occluded. Using one of the deep learning algorithms, YOLOv5, this work tries to identify the face of someone whose face is occluded by a mask in real-time. A special program is being created to test the effectiveness of the YOLOv5 algorithm. 14 people's data were registered, and each person had 150 images used for training, validation, and testing. The images used are regular faces and mask-occluded faces. Nine distinct configurations of epoch and batch sizes were used to train the model. Then, during the testing phase, the best-performing configuration was chosen. Images and real-time input were used for testing. The highest possible accuracy of image identification is 100%, whereas the maximum accuracy of real-time identification is 64%. It was found during the testing that the brightness of the room has an influence on the performance of YOLOv5. Identifying individuals becomes more challenging when there are significant changes in brightness.

CFormerFaceNet: Efficient Lightweight Network Merging a CNN and Transformer for Face Recognition

Most face recognition methods rely on deep convolutional neural networks (CNNs) that construct multiple layers of processing units in a cascaded form and employ convolution operations to fuse local features. However, these methods are not conducive to modeling the global semantic information of the face and lack attention to important facial feature regions and their spatial relationships. In this work, a Group Depth-Wise Transpose Attention (GDTA) block is designed to effectively capture both local and global representations, mitigate the issue of limited receptive fields in CNNs, and establish long-range dependencies among different feature regions. Based on GDTA and CNNs, a novel, efficient, and lightweight face recognition model called CFormerFaceNet, which combines a CNN and Transformer, is proposed. The model significantly reduces the parameters and computational cost without compromising performance, greatly improving the computational efficiency of deep neural networks in face recognition tasks. The model achieves competitive accuracy on multiple challenging benchmark face datasets, including LFW, CPLFW, CALFW, SLLFW, CFP_FF, CFP_FP, and AgeDB-30, while maintaining the minimum computational cost compared to all other advanced face recognition models. The experimental results using computers and embedded devices also demonstrate that it can meet real-time requirements in practical applications.

Predicting the eyebrow from the orbit using three-dimensional CT imaging in the application of forensic facial reconstruction and identification

Eyebrows are the most important facial feature in facial recognition with its shape rated to be more helpful than color or density for facial reconstruction or approximation. However, little extant research has estimated the position and morphological territory of the eyebrow from the orbit. Three-dimensional craniofacial models, produced from CT scans of 180 Koreans autopsied at the National Forensic Service Seoul Institute, were used to conduct metric analyses of subjects (125 males and 55 females) between 19 and 49 (mean 35.1) years. We employed 18 craniofacial landmarks to examine the morphometry of the eyebrow and orbit with 35 pairs of distances per subject measured between landmark and reference planes. Additionally, we used linear regression analyses to predict eyebrow shape from the orbit for every possible combination of variables. The morphology of the orbit has more influence on the position of the superior margin of the eyebrow. In addition, the middle part of the eyebrow was more predictable. The highest point of the eyebrow in female was located more medially than the male. Based on our findings, the equations for estimating the position of the eyebrow from the shape of the orbit is useful information for face reconstruction or approximation.

Quality Metric Guided Portrait Line Drawing Generation From Unpaired Training Data.

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i.e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data. To achieve these benefits, we (1) propose a novel quality metric for portrait drawings which is learned from human perception, and (2) introduce a quality loss to guide the network toward generating better looking portrait drawings. We observe that existing unpaired translation methods such as CycleGAN tend to embed invisible reconstruction information indiscriminately in the whole drawings due to significant information imbalance between the photo and portrait drawing domains, which leads to important facial features missing. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible and only embedded in the selected facial regions. Along with localized discriminators for important facial regions, our method well preserves all important facial features in the generated drawings. Generator dissection further explains that our model learns to incorporate face semantic information during drawing generation. Extensive experiments including a user study show that our model outperforms state-of-the-art methods.

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer's Disease Subjects.

Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more.

Facial Emotion Recognition as Spatial Image using Gabor Filter

The state of mind of a person can be easily understood from the human face. This paper proposes a methodology to recognize facial expression using Gabor filters, ResNet and two other custom models. The image is taken as the input data from the camera. This input can be used to extract information to infer a person's mood. First, we develop an algorithm for detecting image of an individual from entire set of images using Haar Cascade face detection algorithm. Then, we apply Gabor filter for extracting facial features in the spatial domain. Using the Gabor filter can effectively reduce computation and size, and in some situations even improve recognition. Gabor filters are used to capture the entire frequency spectrum in all directions. Finally, facial expressions are successfully classified by proposed Convolutional Neural Network model using extracted important facial features from the facial image after applying Gabor filter as input. The results of testing images from the CK+ dataset show the reliability and the best recognition rate of the proposed method.

Masked-face recognition using deep metric learning and FaceMaskNet-21.

The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. The conventional face recognition systems used for security purposes have become ineffective in the current situation since the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. We have proposed a system that uses the deep metric learning technique and our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, static video files. We achieved a testing accuracy of 88.92% with an execution time of fewer than 10 ms. The ability of the system to perform masked face recognition in real-time makes it suitable to recognize people in CCTV footage in places like malls, banks, ATMs, etc. Due to its fast performance, our system can be used in schools and colleges for attendance, as well as in banks and other high-security zones to grant access to only the authorized ones without asking them to remove the mask.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10489-021-03150-3.

Iterative facial image inpainting based on an encoder-generator architecture

Facial image inpainting is a challenging problem as it requires generating new pixels that include semantic information for masked key components in a face, e.g., eyes and nose. Recently, remarkable methods have been proposed in this field. Most of these approaches use encoder–decoder architectures and have different limitations such as allowing unique results for a given image and a particular mask. Alternatively, some optimization-based approaches generate promising results using different masks with generator networks. However, these approaches are computationally more expensive. In this paper, we propose an efficient solution to the facial image painting problem using the Cyclic Reverse Generator (CRG) architecture, which provides an encoder-generator model. We use the encoder to embed a given image to the generator space and incrementally inpaint the masked regions until a plausible image is generated; we trained a discriminator model to assess the quality of the generated images during the iterations and determine the convergence. After the generation process, for the post-processing, we utilize a Unet model that we trained specifically for this task to remedy the artifacts close to the mask boundaries. We empirically observed that even in the absence of important facial features, the encoder model is capable of embedding images in semantically rich regions in the latent space, utilizing the surrounding context in the images. Cultivating the feedback loop between the encoder and generator gradually improves the missing content in the images in an iterative fashion, and only a few iterations are sufficient to generate realistic content. Since the models are not trained for particular mask types, our method allows applying sketch-based inpaintings, using a variety of mask types, and producing multiple and diverse results. We compared our method with the state-of-the-art models both quantitatively and qualitatively, and observed that our method can compete with the other models in all mask types; it is particularly better in images where larger masks are utilized. Our code, dataset and models are available at: https://github.com/yahyadogan72/iterative_facial_image_inpainting.

Transfer learning based effective emotional face recognition using DCNN via cropping techniques

Facial Expression Recognition (FER) has grown in popularity as a result of the recent advancement and use of humancomputer interface technologies. Because the images can vary in brightness, backdrop, position, etc. it is challenging for current machine learning and deep learning models to identify facial expression. If the database is small, it doesn't operate well. Feature extraction is crucial for FER, and if the derived characteristics can be separated, even a straightforward approach can help tremendously. Deep learning techniques and automated feature extraction, allow some irrelevant features to conflict with important features. In this paper, we deal with limited data and simply extract useful features from images. To make data more numerous and allow for the extraction of just important facial features, we suggest innovative face cropping, rotation, and simplification procedures and advocate using the Transfer Learning technique to construct DCNN for building a very accurate FER system. By replacing the dense top layer(s) with FER, a pretrained DCNN model is adopted, and the model is then modified with facial expression data. The training of the dense layer(s) is followed by adjusting each of the pre-trained DCNN blocks in turn. This new pipeline technique has gradually increased the accuracy of FER to a higher degree. On the CK+ and JAFFE datasets, experiments were run to assess the suggested methodology. For 7-class studies on the CK+ and JAFFE databases, high average accuracy in recognition of 99.49% and 98.58% were acquired.

Image Processing For Face Recognition Using HAAR, HOG, and SVM Algorithms

Face recognition is one of the most active areas of research from the past two decades. Attempts are being made to understand how a human recognizes another human face. It is widely accepted that facial recognition can be based on structural information and nonstructural / spatial details. In the present study, he is applying differential observations using Eigen / docking characteristics of many built-in facial features and artificial neural networks. The proposed method aims to obtain a facial feature by reducing facial features such as eyes, nose, mouth, and face depending on the importance of facial features. The face recognition system developed in this paper will inform the human face and assess the current percentage of accuracy. Therefore, this work is for human facial recognition and includes a percentage of facial expressions. The implementation of this function also offers many applications such as photography, bio-metric in bank Lockers, etc.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.

Application of nasal profile estimation methods for Korean population.

The nose is an important facial feature that affects facial reconstruction quality and its recognition. We reviewed a selection of papers that researched the nose with regression analysis and applied their regression formulae to our sample to compare the estimated value and measurements. One-hundred computed tomography (CT) scans of Korean adults were selected for this study. The subjects were patients without head trauma, severe deformities, or asymmetry at St. Mary's Hospital in Seoul, Korea. Three dimensional (3D) models of the cranium and face were remodelled and measured by Mimics 14.1 (Leuven, Belgium, Materialise). A measurement system for 3D models that referred to landmarks from previous studies was used to facilitate the comparison process. Estimation formulae were applied to the subjects and compared with the measurement results of this study's population. Corresponding measurements showed that the measurements were statistically significantly different from estimated values obtained with regression formulae. A difference in the results was expected because of the different sample population, but the age of subjects could also impact the results and cause variability. Therefore, the results of this study emphasise the necessity of basal research for a specific target population.

Face Image Reflection Removal

Face images captured through glass are usually contaminated by reflections. The low-transmitted reflections make the reflection removal more challenging than for general scenes because important facial features would be completely occluded. In this paper, we propose and solve the face image reflection removal problem. We recover the important facial structures by incorporating inpainting ideas into a guided reflection removal framework, which takes two images as the input and considers various face-specific priors. We use a newly collected face reflection image dataset to train our model and compare with state-of-the-art methods. The proposed method shows advantages in estimating reflection-free face images for improving face recognition.

Development of a Photonumeric Lip Health Scale.

The lips are important facial anatomic features with particular vulnerability to environmental damage, yet they have received little attention in the dermatologic literature. A photonumeric rating scale for clinically assessing lip heath is needed to advance lip research. To develop a photonumeric lip health assessment scale for clinical use that can evaluate the efficacy of products for improving lip health. The VISIA®-CR 4.3 system was used to photograph the frontal face of 103 subjects with Fitzpatrick skin types I–III exhibiting a range of lip health status based on the key characteristics of lip shine, texture, and vermilion border. An expert panel comprising 3 dermatologists independently rated the images based on the proposed rating scale. Images with ≥75% rater agreement were redistributed to the panel for selecting the final images and verification of the final scale. The panel selected 15 images for the final scale: 5 for each of the key characteristics (lip shine, texture, and vermilion border) and 1 for each ordinal rating of 0–5 within a characteristic (eg, 0=very shiny, 5=very dull). All of these images achieved 100% agreement among the raters. This scale provides healthcare professionals and researchers a way to evaluate current lip health, track improvement, and evaluate the efficacy of treatments. It can be used to communicate with patients during discussions about lip conditions, recommending treatments, and setting goals. The scale also provides a research tool to evaluate different formulations for developing lip care products. J Drugs Dermatol. 2020;19(6):   doi:10.36849/JDD.2020.5139.

A Method to Reproduce Symmetry in Midfacial Reconstruction: A Report of 19 Cases.

ABSTRACTBACKGROUNDReconstruction of facial skin defects remains a clinical challenge. With aging, ptosis of tissue over fixed structures creates an important facial feature known as the tear trough. This study aimed to evaluate the efficacy and aesthetic outcome of a novel surgical technique that reproduced this facial feature while avoiding ectropion during midfacial skin defect repair.METHODSNineteen patients with midfacial skin defects received local flap reconstruction combined with an anchoring suture. The flap was designed in a unilateral pedicled V-Y pattern. When the flap was advanced to cover the defect, one or two sutures that connected the dermis of the flap with the infraorbital periosteum were made to reproduce the tear trough line.RESULTS: Midfacial defects were successfully repaired with the V-Y flap in all 19 patients. No lower eyelid ectropion or conspicuous scars were noted in any of the patients. Further, the tear trough was successfully reconstructed in each patient. Facial symmetry was maintained with static positioning and animation.CONCLUSIONSThe combination of local V-Y flap reconstruction with anchoring sutures to reproduce facial feature lines is an effective technique in midfacial skin defect repair.