Face Size Research Articles

The “Fat Face” illusion: A robust adaptation for processing pairs of faces

Converging evidence has demonstrated our remarkable capacities to process individual faces. However, in real-life contexts, we rarely see faces in isolation. It is largely unknown how our visual system processes a multitude of faces. The current study explored this question by using the “Fat Face” illusion: when two identical faces are vertically aligned, the bottom face appears bigger. In Experiment 1, we tested the robustness of this illusion by using faces varied by gender and race, by recruiting participants from different countries (Canadian, Chinese, and French), and by implementing different task requirements. We found that the illusion was stable and immune to variations in face gender or face race, perceptual familiarity, and task requirements. Experiment 2 further indicated that binocular vision was essential for this visual illusion. When participants performed the task with one eye covered, the previously robust illusion completely disappeared. Together, these findings revealed a visual adaptation for processing multiple faces in the environment: the face at the top is perceived as more distant from the viewer and appears smaller in size than the face at the bottom. More broadly, overestimating the size of the bottom face may represent a fundamental mechanism for social interactions, ensuring the deployment of attention to those closest to self.

Geometric Analysis and YOLO Algorithm for Automatic Face Detection System in a Security Setting

Face detection is a computer technology that determines the location and size of the human face in an arbitrary digital image employed for authentication. Face recognition technology plays a vital role in network security, video compression, content indexing, and retrieval since "humans" are the focal point of many videos. Face recognition-based network access control makes it almost difficult for hackers to obtain a user’s "password" and improves user-friendliness in human-computer interaction. In this paper, an automatic face detection system that accurately detects human faces and ignores any other object that is not a human face using geometric analysis and the you-only-look-once (YOLO) algorithm is introduced. The system is able to predict the ages and genders of the faces detected. It also detects the facial landmarks of the faces and indicates the emotions of the faces detected. A sample of four faces is considered for testing the system; thus, accurately detecting gender and emotion but not age correctly. In all, the evaluation shows about 80% accuracy. With the results got, the system can support security and analytics. It can be used to get analytics in an event with a lot of attendees and can also be used to get a facial mapping of someone involved in a crime scene for security purposes.

A Contemporary Look at the Polyhedral Shape Instability of Crystals Growing under Conditions of Diffusion‐Limited Supply of Building Material

AbstractThis paper intends to shed fresh light on the polyhedral shape (in)stability of crystals growing under conditions of uneven diffusion supply of building material. A simplified theoretical consideration of the phenomenon is presented; the whole crystal is regarded as material consumer (but not merely a separate face on it). Accounted is for the fact that different amounts of building material are supplied by diffusion to the diverse types of crystal faces. Besides, critical sizes of crystal faces over which they lose the ability to grow macroscopically flat (because shallow cavities are formed on them) are calculated not only for the basal faces of Zn crystals growing from highly diluted own vapors mixed in inert (in respect to this metal) gases, such as H2 and Ar, but also for the pyramidal crystal faces. So, a logical explanation is given of the long‐standing problem: why the measured critical sizes of Zn single crystals grown in inert atmospheres are much smaller than the theoretically calculated critical sizes? Finally, the widths of the macroscopically flat rims surrounding the cavities on basal faces of Zn and Cd crystals (growing under conditions of uneven diffusion supply of building material) are determined in a simplified way.

Combined fabrication of terahertz hollow-core metal rectangular waveguide cavity using electrochemical deposition and selective chemical dissolution

The terahertz hollow-core metal rectangular waveguide cavity is a typical terahertz rectangular cavity structure that has several advantages and is widely used. However, the fabrication of the terahertz hollow-core metal rectangular waveguide cavity with high working frequency has not made recent breakthroughs, especially when the working frequency is 1-THz and above. In this paper, a combined process of electrochemical deposition and selective chemical dissolution is proposed first to manufacture the terahertz hollow-core metal rectangular waveguide cavity with high working frequency. Taking the fabrication of a 1.7-THz hollow-core metal rectangular waveguide as an example, the manufacturing methods and experiments of each step are described systematically. A terahertz hollow-core metal rectangular waveguide cavity with an end face size of 81.9 × 162.7 µm2, the edge radius less than 10 µm, the internal bottom surface roughness less than 0.08 µm, and the internal side surface roughness less than 0.3 µm is obtained. These experimental results are matched with the processing requirements of a 1.7-THz hollow-core metal rectangular waveguide. This flexible and controllable combined process makes it possible to manufacture more types of terahertz hollow-core metal rectangular cavity structures with high working frequency.

The Influence of Face Masks on Verbal Communication in Persian in the Presence of Background Noise in Healthcare Staff.

Wearing face masks has resulted in verbal communication being more challenging during the COVID-19 pandemic. This study aimed to investigate the effect of face masks on the speech comprehensibility of Persian nurses in healthcare settings. Twenty female nurses from the governmental hospitals randomly participated in an experiment on seven typical commercial face masks at two background noise levels. Nurses' speech intelligibility from a human talker when wearing each face mask was determined based on the speech discrimination score. The vocal effort of nurses wearing each face mask was determined based on the Borg CR10 scale. Based on the linear mixed model, the speech intelligibility of nurses from a human speaker wearing surgical masks, N95 masks, and a shield with face masks were approximately 10%, 20%, and 40–50% lower, respectively, than no-mask conditions (p < 0.01). The background noise decreased the speech intelligibility of nurses by approximately 22% (p < 0.01). The use of a face shield further decreased speech intelligibility up to 30% compared to using a face mask alone (p < 0.01). The vocal efforts of nurses when wearing surgical masks were not significant compared with the baseline vocal efforts (p > 0.05); however, vocal efforts of nurses when wearing N95 and N99 respirators were at an unacceptable level. The face masks had no considerable effect on the speech spectrum below 2.5 kHz; however, they reduced high frequencies by different values. Wearing face masks has a considerable impact on the verbal communication of nurses in Persian. The level of background noise in the healthcare setting can aggravate the effect sizes of face masks on speech comprehensibility.

A Comprehensive Survey on Face Detection Techniques

The need for automatic understanding and examination of data increased with the tremendous growth of video and imaging databases. The change of identity, feelings and attitudes of a person's face always play a key role in terms of social communication. It is difficult for human beings to distinguish and identify various faces. Hence, we can say that in face recognition, the automatic computer-aided face detection system plays an important role. It also plays a significant role in determining the facial expressions and their recognition, estimation of head pose and interaction of humans and computers, etc. The size and location of the human face in a digital image are determined by face detection. For face detection in digital images, this paper brings forward a detailed and comprehensive survey of various important techniques. In this paper, different challenges and applications regarding face detection are also discussed. The standard databases for the detection of the face are mentioned along with various other features. Along with this, special discussions are provided regarding highly practical aspects for the robustness of the system for face detection. In the end, there ¬are some highly promising directions for the research and investigation to be conducted in the future.

Biomechanical correlates of zygomaxillary-surface shape in papionin primates and the effects of hard-object feeding on mangabey facial form

Extant African papioninans are distinguished from macaques by the presence of excavated facial fossae; however, facial excavation differs among taxa. Mangabeys (Cercocebus, Rungwecebus, and Lophocebus) exhibit fossae that invade the zygomatic forming pronounced suborbital fossae (SOFs). Larger-bodied Papio, Mandrillus, and Theropithecus have lateral rostral fossae with minimal/absent suborbital fossae. Because prior studies have shown that mangabeys exhibit adaptations to anterior dental loading (e.g., palatal retraction), it is plausible that mangabey SOFs represent structural accommodation to masticatory-system shape rather than facial allometry, as commonly hypothesized. We analyzed covariation between zygomaxillary-surface shape, masticatory-system shape, and facial size in 141 adult crania of Macaca fascicularis, Papio kindae, Cercocebus, and Lophocebus. These taxa represent the range of papionin SOF expression while minimizing size variation (narrow allometry). Masticatory-system landmarks (39) registered palate shape, bite points, masticatory muscle attachments, and the temporomandibular joint. Semilandmarks (450) captured zygomaxillary-surface shape. Following Procrustes superimposition with semilandmark sliding and principal components analyses, multivariate regression was used to explore allometry, and two-block partial least-squares analyses (within-configuration and separate-blocks) were used to examine covariation patterns. Scores on principal components 1–2 and the first partial least-square (PLS1) separate mangabeys from Macaca and Papio. Both zygomaxillary-surface shape and masticatory-system shape are correlated with size within taxa and facial morphotypes; however, regression distributions indicate morphotype shape differences are non-allometric. PLS1 accounts for ∼95% of shape covariance (p < 0.0001) and shows strong linear correlations (r-PLS = ∼0.95, p < 0.0001) between blocks. Negative PLS1 scores in mangabeys reflect deep excavation of the suborbital malar surface, palatal retraction, and anterior displacement of jaw adductor muscles and the temporomandibular joint. Neither PC1 nor PLS1 scores ordinate specimens by facial size. Taken together, these results fail to support the allometric hypothesis but suggest that mangabey zygomaxillary morphology is closely linked with adaptations to hard-object feeding.

A Framework for Mask-Wearing Recognition in Complex Scenes for Different Face Sizes

People are required to wear masks in many countries, now a days with the Covid-19 pandemic. Automated mask detection is very crucial to help identify people who do not wear masks. Other important applications is for surveillance issues to be able to detect concealed faces that might be a safety threat. However, automated mask wearing detection might be difficult in complex scenes such as hospitals and shopping malls where many people are at present. In this paper, we present analysis of several detection techniques and their performances. We are facing different face sizes and orientation, therefore, we propose one technique to detect faces of different sizes and orientations. In this research, we propose a framework to incorporate two deep learning procedures to develop a technique for mask-wearing recognition especially in complex scenes and various resolution images. A regional convolutional neural network (R-CNN) is used to detect regions of faces, which is further enhanced by introducing a different size face detection even for smaller targets. We combined that by an algorithm that can detect faces even in low resolution images. We propose a mask- wearing detection algorithms in complex situations under different resolution and face sizes. We use a convolutional neural network (CNN) to detect the presence of the mask around the detected face. Experimental results prove our process enhances the precision and recall for the combined detection algorithm. The proposed technique achieves Precision of 94.5%, and is better than other techniques under comparison.

Detect Faces Efficiently: A Survey and Evaluations

Face detection is to search all the possible regions for faces in images and locate the faces if there are any. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the location and the size of faces are known in the image. In recent decades, researchers have created many typical and efficient face detectors from the Viola-Jones face detector to current CNN-based ones. However, with the tremendous increase in images and videos with variations in face scale, appearance, expression, occlusion and pose, traditional face detectors are challenged to detect various "in the wild" faces. The emergence of deep learning techniques brought remarkable breakthroughs to face detection along with the price of a considerable increase in computation. This paper introduces representative deep learning-based methods and presents a deep and thorough analysis in terms of accuracy and efficiency. We further compare and discuss the popular and challenging datasets and their evaluation metrics. A comprehensive comparison of several successful deep learning-based face detectors is conducted to uncover their efficiency using two metrics: FLOPs and latency. The paper can guide to choose appropriate face detectors for different applications and also to develop more efficient and accurate detectors.

Comparison of Real-Time Face Detection and Recognition Algorithms

With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.

Effect of Sugar Stick Fascia Therapy on Changes in Face Size and Skin Condition

This study aimed to comparatively analyze changes in facial size and skin condition after sugar stick fascia therapy and manual technique. Selecting 11 people for sugar stick fascia therapy and 11 people for manual technique, their facial size and skin condition were measured three times before care, after four-times of care, and after eight-times of care. The results were evaluated through survey after experiment. First, regarding the differences in each period and group, the sizes of section A and B were more significantly reduced after sugar stick fascia therapy rather than manual technique, which showed the face downsizing effect (p&lt;.05). Second, regarding the differences in each period, the sugar stick fascia therapy significantly increased moisture and elasticity. Regarding the differences in each group, the moisture was more significantly increased after eight times of sugar stick fascia therapy than manual technique (p&lt;.05). In the evaluation of results, the face downsizing effect and overall satisfaction were highly shown (p&lt;.01). The sugar stick fascia therapy showed great effects on downsizing the cheeks with lots of muscles, and also showed the remarkable increase in moisture. Thus, the sugar stick fascia therapy could be suggested as an alternative care that could meet the desire for beauty, by utilizing it as a face downsizing program without side effects and a skin care program for dry skin.

Too Close to Like?: How Social Media Influencers recast personal space between selfies and Instagram users

ABSTRACT A selfie, as a form of self-presentation, is known to display an “ideal” self. Selfies publicly posted on Instagram were analyzed to see if the size of faces in selfies affects the perceived privacy boundary between selfie takers and viewers, and eventually leads to social media users’ negative evaluation toward selfies. The data showed that close-up selfies were associated with fewer likes received from other users, while full shot selfies received more likes. Meanwhile, influencers with a high number of followers moderated the perception of privacy boundaries. Theoretical and practical implications of these phenomena are also discussed.

A Face Recognition Algorithm Based on Intermediate Layers Connected by the CNN

Face recognition is difficult due to the higher dimension of face image features and fewer training samples. Firstly, in order to improve the performance of feature extraction, we inventively construct a double hierarchical network structure convolution neural network (CNN) model. The front-end network adopts a relatively simple network model to achieve rough feature extraction from input images and obtain multiple suspect face candidate windows. The back-end network uses a relatively complex network model to filter the best detection window and return the face size and position by nonmaximum suppression. Then, in order to fully extract the face features in the optimal window, a face recognition algorithm based on intermediate layers connected by the deep CNN is proposed in this paper. Based on AlexNet, the front, intermediate and end convolution layers are combined by deep connection. Then, the feature vector describing the face image is obtained by the operation of the pooling layer and the full connection layer. Finally, the auxiliary classifier training method is used to train the model to ensure the effectiveness of the features of the intermediate layer. Experimental results based on open face database show that the recognition accuracy of the proposed algorithm is higher than that of other face recognition algorithms compared in this paper.

Abstract 9186: Maslinic Acid Alleviates Atherosclerosis by Suppressing Mitochondrial ROS-Dependent NLRP3 Inflammasome Activation in Vascular Endothelial Cells via SIRT1/Nrf2 Pathway

Introduction: Maslinic acid (MA), a pentacyclic triterpenoid primarily found in food plants, has exhibited multiple inhibitory effects on proatherogenic factors, such as obesity, dyslipidemia, and inflammation. However, its effect on atherosclerosis remains unknown. In this study, we determined if MA can inhibit atherosclerosis progression and explored the underlying molecular mechanisms. Methods: To assess the anti-atherogenic effect of MA in vivo, ApoE deficient mice were treated with MA contained in high-fat diet for 16 weeks. After treatment, mouse aorta and serum samples were collected to determine atherosclerotic lesions, lipid profile, mitochondrial morphology and expression of related molecules. Additionally, human aortic endothelial cells (HAECs) were treated with ox-LDL to induce the endothelial injury model. Prior to the procedure, vehicle or MA were treated in the absence or presence of a SIRT1 inhibitor nicotinamide (NAM) and an Nrf2 inhibitor ML385. Concurrently, we investigated the effects and mechanisms of MA on mitochondrial dysfunction, oxidative stress injury and apoptosis in HAECs. Results: In vivo, MA conferred an anti-atherosclerotic effect as shown by reduced en face and aortic root sinus lesions size. From the electron microscopic analysis, MA ameliorated the mitochondrial damage in aortic endothelium. MA could promote the protein expression of SIRT1 and Nrf2, and enhance the expression of mitochondrial antioxidant enzymes. In addition, MA suppressed NLRP3 inflammasome activation to reduce apoptosis in vascular endothelium. In vitro, MA could inhibit ox-LDL-induced mitochondrial ROS generation by activating SIRT1/Nrf2 pathway, thereby inhibiting NLRP3 inflammasome activation and apoptosis in HAECs. NAM and ML385 eliminated the MA-mediated endothelial protective effect by restraining the expression of SIRT1 and Nrf2, respectively. Conclusions: In conclusion, our findings suggested that MA ameliorated endothelial apoptosis by suppressing mitochondrial ROS-dependent NLRP3 inflammasome activation via SIRT1/Nrf2 pathway and showed efficacy in exerting an anti-atherogenic effect.

Modelling of Facial Images for Analysis of Recognition System

Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.

Association of 3-dimensional facial changes and height and weight increase in children: A 2-year follow-up

This study aimed to evaluate changes in facial size and shape in children and their relationship to the changes in height and weight. One hundred and thirteen healthy children aged between 6 and 13years were followed annually for 2 consecutive years. The facial morphology was captured in 12-month intervals (from T1 to T2 and from T2 to T3) using a 3-dimensional stereophotogrammetric optical scanner; the body height and weight were recorded simultaneously. The changes in facial size and shape were analyzed with geometric morphometrics. Multiple regression mixed-effects models were exploited for evaluation of the association between the changes of facial size or shape and age at the beginning of the observation, gender, and change of height and weight. The centroid size (reflecting facial size) increased from T1 to T2 and T2 to T3 in boys and girls. In contrast, the facial shape did not change during both 12-month observation periods (T1 to T2 and T2 to T3) either in boys or girls. Of 2 multiple regression mixed-effects models, only the model with the change of natural logarithm of centroid size as a dependent variable was statistically significant (P<0.001; adjusted r2=0.29). It showed that height and weight changes were associated with a change of the facial size (with weight change having a greater effect than height change: adjusted r2=0.25 for weight change and adjusted r2=0.106 for height change). Most changes in the facial morphology observed in our cohort were associated with increasing facial size. In contrast, the shape of the face remained relatively constant. Body height and weight gains were associated with the change of the facial size only. However, only 29% of the variation in facial size was explained by height or weight changes during growth.

Face dominance modulates the perceived face size: converging evidence from three countries

Face dominance modulates the perceived face size: converging evidence from three countries

Multi-Criteria Optimization of Energy-Efficient Cementitious Sandwich Panels Building Systems Using Genetic Algorithm

This paper presents results of a study that focuses on developing a genetic algorithm (GA) for multi-criteria optimization of orthotropic, energy-efficient cementitious composite sandwich panels (CSP). The current design concept of all commercially produced CSP systems is based on the assumption that such panels are treated as doubly reinforced sections without the consideration of the three-dimensional truss contribution of the orthotropic panel system. This leads to uneconomical design and underestimating both the strength and stiffness of such system. In this study, two of the most common types of commercially produced sandwich were evaluated both numerically and experimentally and results were used as basis for developing a genetic algorithm optimization process using numerical modeling simulations. In order to develop a sandwich panel with high structural performance, design optimization techniques are needed to achieve higher composite action, while maintaining the favorable features of such panels such as lightweight and high thermal insulation. The study involves both linear and nonlinear finite element analyses and parametric optimization. The verification and calibration of the numerical models is based on full-scale experimental results that were performed on two types of commercially produced sandwich panels under different loading scenarios. The genetic algorithm technique is used for optimization to identify an optimum design of the cementitious composite sandwich panels. The GA technique combines Darwin’s principle of survival of fittest and a structured information exchange using randomized crossover operators to evolve an optimum design for the cementitious sandwich panel. Parameters evaluated in the study include: (i) shear connectors’ geometry, its volume fraction and distribution; (ii) exterior cementitious face sheets thickness and (iii) size and geometry steel wires reinforcements. The proposed optimization method succeeded in reducing cost of materials of CSP by about 48% using genetic algorithm methodology. In addition, an optimized design for CSP is proposed that resulted in increasing the panel’s thermal resistance by 40% as compared to existing panels, while meeting ACI Code structural design criteria. Pareto-optimal front and Pareto-optimal solutions have been identified. Correlation between the design variables is also verified and design recommendation are proposed.

The Designing, Testing, and Utility of a 3D-Printed Respirator: A Hospital's Journey Into Self-Sustainability During COVID-19.

ObjectiveThe current global COVID-19 pandemic has disrupted supply chains and the production of essential goods and services. This includes personal protective equipment (PPE) kits, respirators, and other protective devices. Hence efforts were made to prototype and produce 3D-printed N95 respirators to fill the gap in supply. In addition, methods of sterilization were put into place for the respirators. As well as forming standard operating procedures.MethodsWith the use of vast open-source libraries and collaboration with engineers and doctors fighting the COVID-19 pandemic, respirator prototypes were produced with special consideration to the sizing to fit median facial sizes. Polymer plastics were mixed in various proportions to condition the respirator to be used by frontline workers in austere environments. Due to the shortage of medical-grade filter media, alternative sources were researched. Merv 13 and Merv 15 filters were selected due to their cheap costs, vast abundance, and proven filtration efficacy against particles of 0.03 microns. Studies conducted around the world have also shown its efficacy as an alternative to medical-grade air filter media. After developing standard operating procedures (SOPs) for sterilisation and respirator usage. Emergency approval was obtained and a limited number of healthcare workers were issued with this respirator (n=400). PPE kit satisfaction and self-efficacy scores were calculated from daily questionnaires during donning and doffingResultsQualitative fit-tests in all 400 healthcare workers matched those of a conventional N95 respirator. Almost all of the respondents in the PPE kit satisfaction responded positively. The self-efficacy score calculated from the general self-efficiency scale had an overall positive value, with the average score being 4.29. This demonstrated that the self-efficacy score was above average and indicated a high motivation to overcome obstacles and spend more time solving problems. The average self-efficacy score is defined between 2.5 - 3.5, and a low self-efficacy score is defined as a score below 2.5. Lastly, a regression analysis was done to test the correlation between PPE kit satisfaction and self-efficiency this demonstrated a positive correlation between PPE kit satisfaction using the 3D-printed respirator and self-efficacy (Slope: 0.416, Intercept: -1.066, R-value: 0.872, P-value: <0.01)ConclusionsWith supply chain disruptions and reduced or nonexistent supplies of essential medical goods. The need of a reusable, sterilisable, and efficient respirator has never been more evident. The materials used have made it sustain heavy use in austere environments. Studies have reported higher than average burnout rates in COVID-19-based healthcare workers. Studies have also shown that the rates of burnout are high in healthcare professionals without access to proper PPE kits in developing nations. This respirator was rated highly in PPE kit satisfaction and the self-efficacy score. Studies have demonstrated a correlation between high self-efficacy scores and low burnout rates in health care workers. There is also documented evidence of a positive correlation between high self-efficacy scores and general health. As the pandemic continues to evolve, so will the efforts to combat it, such as 3D printing. Interdisciplinary collaboration continues to drive our efforts to combat the pandemic and hopefully resolve it in the future.

Facial Asymmetry in Unilateral Congenital Superior Oblique Muscle Palsy.

This study was conducted to evaluate facial asymmetry in unilateral congenital superior oblique muscle palsy (SOP). The results showed that all facial asymmetry parameters had a higher frequency in SOP patients compared with orthotropic individuals. This study aimed to evaluate the characteristics of facial asymmetry in unilateral congenital SOP and compare with orthotropic individuals. This cross-sectional comparative case series was conducted in 58 patients with ocular torticollis caused by SOP (mean ± standard deviation age, 18 ± 12 years) and 58 orthotropic individuals (mean ± standard deviation age, 19 ± 13 years). The exact form of torticollis was determined by direct observation from yaw, roll, and pitch axes. Four photographs were taken from patients: (1) with torticollis to calculate the amount of head tilt; (2) with the head in the straight position to calculate the facial angle and relative facial size (RFS); and (3 and 4) with the head positioned downward (to compare the cheek size) and upward (to assess columella deviation and nostril asymmetry). Twenty-nine patients (50%) had a head tilt, 23 (39.66%) had combined head tilt and a face turn, and 6 (10.44%) had a pure face turn. The mean ± standard deviation of head tilt, facial angle, and RFS was 10.11 ± 6.31°, 1.11 ± 1.67°, and 1.003 ± 0.126 in SOP patients, respectively, and the mean RFS and facial angle were significantly higher in SOP patients compared with orthotropic individuals (both P < .001). Facial hemihypoplasia, unilateral cheek compression, nostril asymmetry, and columella deviation were observed in 43 (74.1%), 31 (53.4%), 39 (67.2%), and 38 patients (65.5%), respectively, which were all significantly more common compared orthotropic individuals (P < .001). Facial asymmetry was seen in 52 patients (91.2%) and 17 orthotropic subjects (29.3%), respectively (P < .001). All quantitative and qualitative facial asymmetry parameters had a higher frequency in SOP patients compared with orthotropic subjects.