Facial Patterns Research Articles

Eye-Tracking Methodology to Detect Differences in Attention to Faces Between Developmental Language Disorder and Autism.

Developmental language disorder (DLD) and autism sometimes appear as overlapping conditions in behavioral tests. There is much literature on the visual scanning pattern (VSP) of faces in autistic children, but this is scarce regarding those with DLD. The purpose of this study was to compare the VSP of faces in young children with DLD, those with autism, and typically developing peers, assessing the effect of three variables. Two eye-tracking experiments were designed to assess the effect of the emotion and the poser's gender (Experiment 1) and the poser's age (Experiment 2) on the VSP of participants (Experiment 1: N = 59, age range: 32-74 months; Experiment 2: N = 58, age range: 32-74 months). We operationalized the VSP in terms of attentional orientation, visual preference, and depth of processing of each sort of face. We developed two paired preference tasks in which pairs of images of faces showing different emotions were displayed simultaneously to compete for children's attention. Data analysis revealed two VSP markers common to both disorders: (a) superficial processing of faces and (b) late orientation to angry and child faces. Moreover, one specific marker for each condition was also found: typical preference for child faces in children with DLD versus diminished preference for them in autistic children. Considering the similarities found between children with DLD and those with autism, difficulties of children with DLD in attention to faces have been systematically underestimated. Thus, more effort must be made to identify and respond to the needs of this population. Clinical practice may benefit from the potential of eye-tracking methodology and the analysis of the VSP to assess attention to faces in both conditions. This would also contribute to the improvement of early differential diagnosis in the long run.

A HYBRID ASSAULT DETECTION SYSTEM USING RANDOM FOREST ENABLED XGBOOST-LIGHTGBM TECHNIQUE

This article presents the development of an assault identification system using face recognition in a closed location, by employing a machine learning-based computer vision approach. The proposed model combines algorithms such as Random forest, XGBoost and LightGBM techniques. The objective is to accurately identify and classify instances of assault in real-time based on facial recognition. The proposed approach utilizes machine learning algorithms to analyze facial features and patterns associated with assault activities. By leveraging on a hybrid model, the system can be integrated into closed locations such as schools, workplaces, or public venues to enhance security measures and promptly respond to potential threats. The findings of this research contribute to the field of computer vision-based assault identification systems, in addressing security challenges. Further advancements of the proposed hybrid model can lead higher performance levels in various real-world scenarios and enhancing public safety and security. The system's performance was evaluated using various metrics, including precision, recall, F1 score, accuracy, and ROC score. The results shows that the proposed system outperformed the existing system with its identified weakness and limitations of: Limited Robustness in Handling Complex Variations, Inability to Handle High-Dimensional Data, Limited Scalability, e.tc. The hybrid model achieved impressive results, with a precision of 98%, recall of 98%, F1 score of 97.7%, accuracy of 97.5%, and ROC score of 97.4%. The above findings demonstrated the effectiveness and robustness of the developed system in accurately detecting and recognizing assault instances within a closed location.

Midpalatal Suture Maturation in Relation to Age, Sex, and Facial Skeletal Growth Patterns: A CBCT Study.

The evaluation of midpalatal suture maturation is essential to undertake the most predictable maxillary expansion approach. Several factors, such as age, gender, and facial growth patterns, seem to be involved in midpalatal suture staging and, consequently, in its opening; however, the link between these variables and the stages of midpalatal suture development remains poorly understood. Our study aimed to analyse the midpalatal suture maturation in relation to age, sex, and skeletal growth patterns by CBCT. We enrolled 263 patients (119 males and 144 females) aged from 8 to 20 years. The midpalatal suture maturation was defined according to Angelieri et al.'s classification using a low-dose CBCT. The chi-square test and linear regression were applied to investigate the suture stages by age, sex, and vertical and sagittal growth patterns. Stage A was present in 8- and 9-year-olds with a larger prevalence in boys, while the prevalence of stage E increased progressively with age. Stage D was the most prevalent in our sample. The statistical analysis described that stage A was more likely in the youngest subjects, and stage E in the oldest participants. The males tended to have lower maturation stages. Moreover, the hypodivergent and normodivergent subjects tended to have higher maturation stages, while Class III was more likely in subjects in stages D or E. A total of 127 patients were in stages A, B, and C, showing an unfused suture. In young individuals, the opening of the midpalatal suture leads to a proper facial growth development by correcting the transverse superior hypoplasia. The midpalatal sutural maturation classification was related to age, sex, and divergence.

To evaluate the cortical bone thickness for ideal placement of infrazygomatic crest (IZC) and mandibular buccal shelf (MCS) bone screws in patients with different facial pattern using cone-beam computed tomography (CBCT)

This study aimed to measure the cortical bone thickness at different anatomical sites of InfraZygomatic Crest and Mandibular Buccal Shelf region for the ideal placement of IZC and MBS screws respectively in patients with different facial patterns using CBCT.Total participants were divided into 3 groups- GROUP A: horizontal growth pattern (FMA <22°); GROUP B: average growth pattern (FMA 22°-28°); GROUP C: vertical growth pattern(FMA >28°). Sixty CBCT images were taken into consideration. They were converted into standard DICOM format. Bone thickness were measured in the InfraZygomatic Crest (IZC) of the maxilla and in the Mandibular Buccal Shelf (MBS) region of the mandible using the Kodak CareStream CS 9300 Software.The results indicate that there was a highly statistical significant difference seen in the overall cortical bone thickness of IZC and MBS at various anatomical sites. The highest mean thickness was found to be at the MB70◦ maxillary 1st molar and distal root of the mandibular 1st molar. There was no difference in the bone thickness at the IZC region and at the MBS region seen between the horizontal and vertical facial patterns whereas the average facial patterns show the highest mean thickness but was not statistically significantThe study conducted on the Indian population yielded results demonstrating the possibly appropriate position for the IZC bonescrews and MBS bonescrews suggested that placing the IZC bonescrews at the mesiobuccal root of the maxillary 1st molar at an angulation of 70◦ and the MBS bonescrews at the distal root of the mandibular 1st molar at 11mm from the cementoenamel junction irrespective of the facial patterns could provide positive results.

Viewpoint independent face recognition via extrapolation in paper wasps.

Visual recognition of three-dimensional signals, like faces, is challenging because the signals appear different from different viewpoints. A flexible, but cognitively challenging solution is viewpoint independent recognition, where receivers identify signals from novel viewing angles. Here, we use same/different concept learning to test viewpoint independent face recognition in Polistes fuscatus, a wasp that uses facial patterns to individually identify conspecifics. We find that wasps use extrapolation to identify novel views of conspecific faces. For example, wasps identify a pair of pictures of the same wasp as the 'same', even if the pictures are taken from different views (e.g. one face 0° rotation, one face 60° rotation). This result is notable because it provides the first evidence of view invariant recognition via extrapolation in an invertebrate. The results suggest that viewpoint independent recognition via extrapolation may be a widespread strategy to facilitate individual face recognition.

Advancing Virtual Interviews: AI-Driven Facial Emotion Recognition for Better Recruitment

Behavior analysis involves the detailed process of identifying, modeling, and comprehending the various nuances and patterns of emotional expressions exhibited by individuals. It poses a significant challenge to accurately detect and predict facial emotions, especially in contexts like remote interviews, which have become increasingly prevalent. Notably, many participants struggle to convey their thoughts to interviewers with a happy expression and good posture, which may unfairly diminish their chances of employment, despite their qualifications. To address this challenge, artificial intelligence techniques such as image classification offer promising solutions. By leveraging AI models, behavior analysis can be applied to perceive and interpret facial reactions, thereby paving the way to anticipate future behaviors based on learned patterns to the participants. Despite existing works on facial emotion recognition (FER) using image classification, there is limited research focused on platforms like remote interviews and online courses. In this paper, our primary focus lies on emotions such as happiness, sadness, anger, surprise, eye contact, neutrality, smile, confusion, and stooped posture. We have curated our dataset, comprising a diverse range of sample interviews captured through participants' video recordings and other images documenting facial expressions and speech during interviews. Additionally, we have integrated existing datasets such as FER 2013 and the Celebrity Emotions dataset. Through our investigation, we explore a variety of AI and deep learning methodologies, including VGG19, ResNet50V2, ResNet152V2, Inception-ResNetV2, Xception, EfficientNet B0, and YOLO V8 to analyze facial patterns and predict emotions. Our results demonstrate an accuracy of 73% using the YOLO v8 model. However, we discovered that the categories of happy and smile, as well as surprised and confused, are not disjoint, leading to potential inaccuracies in classification. Furthermore, we considered stooped posture as a non-essential class since the interviews are conducted via webcam, which does not allow for the observation of posture. By removing these overlapping categories, we achieved a remarkable accuracy increase to around 76.88% using the YOLO v8 model.

Macrovibrissae and microvibrissae inversion and lateralization in elephants.

Elephants are known for strongly lateralized trunk behaviors, but the mechanisms driving elephant lateralization are poorly understood. Here, we investigate features of elephant mouth organization that presumably promote lateralization. We find the lower jaw of elephants is of narrow width, but is rostrally strongly elongated even beyond the jaw bone. Elephant lip vibrissae become progressively longer rostrally. Thus, elephants have two lateral dense, short microvibrissae arrays and central, less dense long macrovibrissae. This is an inversion of the ancestral mammalian facial vibrissae pattern, where central, dense short microvibrissae are flanked by two lateral macrovibrissae arrays. Elephant microvibrissae have smaller follicles than macrovibrissae. Similar to trunk-tip vibrissae, elephant lip microvibrissae show laterally asymmetric abrasion. Observations on Asian zoo elephants indicate lateralized abrasion results from lateralized feeding. It appears that the ancestral mammalian mouth (upper and lower lips, incisors, frontal microvibrissae) is shaped by oral food apprehension. The elephant mouth organization radically changed, however, because trunk-mediated feeding replaced oral apprehension. Such elephant mouth changes include the upper lip-nose fusion to the trunk, the super-flexible elongated lower jaw, the loss of incisors, and lateral rather than frontal microvibrissae. Elephants' specialization for lateral food insertion is reflected by the reduction in the centering effects of oral food apprehension and lip vibrissae patterns.

Nerve entry points in the mimic musculature of the horse head.

Facial expressions are important in pain recognition in horses, but current observation-based pain scales remain subjective. A promising technique to quantitatively measure subtle changes in expression patterns, including changes invisible to the human eye, is surface electromyography (sEMG). To achieve high-quality and reliable sEMG signals, unilateral placement of bipolar electrodes is required in relation to the motor endplates (MEP). We aimed to localize the nerve entry points (NEPs; where the nerve branch first pierced the muscle belly) and the direction of the terminal nerve endings to estimate MEP locations of the innervating nerves in five equine facial muscles involved in pain expression. Three cadaveric Dutch Warmblood horse heads were dissected to identify the NEPs in the musculi caninus, levator anguli oculi medialis, nasolabialis, masseter and zygomaticus. These points were marked with pins and measured in relation to a reference line between two anatomical landmarks near the origin and insertion of the respective muscle. Relative distances were calculated from the most caudally situated landmark. NEPs were located at 33%-38% (caninus), 69%-86% (levator anguli oculi medialis) and 0%-18% (zygomaticus) from the caudal landmark. The nasolabialis showed two innervations zones. Its NEPs were located at 47%-72% (dorsal muscle branch) and 52%-91% (ventral branch). All terminal nerve endings were found to run in rostral direction. The masseter showed numerous NEPs diffusely spread within the muscle belly. Therefore, calculation of relative positions was not performed. These results could form the basis for feasibility studies and standardization of bipolar electrode positioning invivo to measure facial muscle activity patterns in horses.

How an Android Expresses “Now Loading…”: Examining the Properties of Thinking Faces

The “thinking face” is a facial signal used to convey being in thought. For androids, the thinking face may be important to achieve natural human–robot interaction. However, the facial pattern necessary for portraying the thinking face remains unclear and has not yet been investigated in androids. The current study aims to (a) identify the facial patterns when people are engaged in answering complex questions (i.e., thinking face) and (b) clarify whether implementing the observed thinking faces in an android can facilitate natural human–robot interaction. In Study 1, we analyze the facial movements of 40 participants after they are prompted with difficult questions and indicate five facial patterns that corresponded to thinking faces. In Study 2, we further focus on the pattern of furrowing of the brows and narrowing of the eyes among the observed thinking facial patterns and implement this pattern in an android. The results show that thinking faces enhance the perception of being in thought, genuineness, human-likeness, and appropriateness in androids while decreasing eeriness. The free-description data also revealed that negative emotions are attributed to the thinking face. In Study 3, we compared the thinking vs. neutral faces in a question–answer situation. The results showed that the android's thinking face facilitated the perception of being in thought and human-likeness. These findings suggest that the thinking face of androids can facilitate natural human–robot interaction.

Model test on the effects of shield machine cutterhead vibration on tunnel face stability in sandy ground

Face stability is one of the essential problems in shield tunneling. When tunneling in cobble stratum or mixed face ground conditions, significant cutting-induced cutterhead vibration would occur and affect the face stability. To reveal the mechanism and effect of vibration on the tunnel face stability, a transparent tunnel model with a movable vibration exciter was designed and a series of model tests were performed under different vibration magnitudes Aa and frequencies f. Meanwhile, particle image velocimetry was used to reveal the displacement field and the failure pattern of the tunnel face. The test results indicate that the cutting-induced vibration produces a significant reduction effect on the tunnel face stability, as expressed by the increase of the face support pressure and the failure zone when the vibration magnitude and frequency increase. Compared with the static unloading conditions, the width of the failure wedge Lwt increased by about 5.75% and 35.66% for the loose and dense sand, respectively, under dynamic unloading conditions (Aa = 0.2 g, f = 10 Hz). The limit support pressure increased up to about 0.20γD at a vibration of 0.3 g and 50 Hz, much larger than those of static conditions, which were about 0.08γD–0.09γD. An observable self-stabilizing arch can be formed in dense sand under static unloading conditions, while under dynamic unloading conditions, the long-time stable soil arch would not occur. The contributions of this paper could provide an insightful understanding of the effects of cutterhead vibration on tunnel face stability.

Accuracy of geometric morphometrics for age estimation using frontal face photographs of children and adolescents: A promising method for forensic practice

Age estimation is crucial in legal and humanitarian contexts. Forensic professionals may use various procedures to estimate age, including dental analysis, bone density tests, evaluation of physical characteristics including facial bone structure and development, and image-based methods. Although images are often the only material available, visual observation of photographic material is an imprecise method in age estimation, which can compromise judicial decision-making. Analyzing 4000 photographs from the Brazilian Federal Police database, representing four age groups (6, 10, 14, and 18 years), the study employed automated analysis by marking 28 photogrammetric points. Data were used to establish facial patterns by age and sex using the facial geometric morphometrics method. Performance was assessed through a Multinomial Logistic Regression model, evaluating accuracy, sensitivity, and specificity across the categorical age groups. Analyses were conducted using R software, with a 5 % significance level. The study found that facial geometric morphometrics achieved an overall accuracy of 69.3 % in age discrimination, with higher accuracy in males (74.7 %) compared to females (65.8 %) (p < 0.001). The method excelled at predicting the age of 6-year-olds with 87.3 % sensitivity and 95.6 % specificity but had lower performance at 14 years. It showed greater accuracy in distinguishing age groups with larger age gaps, achieving up to 99.5 % accuracy between certain groups, and was particularly effective in differentiating ages of 6 and 10 years in females and 10, 14, and 18 years in males. The facial geometric morphometrics emerges as a promising approach for age estimation among children and adolescents in forensic settings.

Micro-expression recognition based on a novel GCN-transformer cooperation model for IoT-eHealth

If a person is truly healthy, his/her well-being encompasses both physical and psychological health. However, the existing IoT-eHealth system typically focus only on monitoring the user’s physical data through various sensors, neglecting their mental state. To enhance the intelligence level of IoT-eHealth system and enable it to have the psychological monitoring ability, a novel collaborative model based on Graph Convolutional Network (GCN) and Transformer is designed for Micro-Expression (ME) recognition in this paper. Firstly, facial information within each frame is transformed into a Spatial Topological Relationship Graph (STRG) by using facial landmarks detection and psychological relationship of local patches. Then, in order to automatically aggregate the key information on facial patches that contribute to ME recognition from the structured graph data, a Hierarchical Adaptive Graph Pooling (HAGP) module is designed for obtaining discriminative frame-level feature based on GCN utilizing graph structure and vertex global dependencies. Finally, in order to model the long-term dependencies among frames and capture the key frame-level features that are beneficial for ME recognition, a Temporal Sensitive Self-Attention (TSSA) mechanism is designed, and a novel Temporal Sensitive Transformer (TST) encoder is constructed based on TSSA to explore the evolution law of facial patterns and obtain discriminative video-level features that are helpful for ME recognition. In the comparative experiments of standard dataset verification and practical dataset testing, designed collaborative model is superior to other methods and can achieve the highest recognition accuracy, which almost can meet the application requirements of IoT-eHealth system.

Dentoalveolar bone height in Class I adults with different vertical patterns: A cross-sectional study

Facial divergence during growth and development affects both the anterior and posterior alveolar bone dimensions in the maxilla and mandible, and the dentoalveolar compensation mechanism in severe vertical skeletal discrepancies remains unclear. To evaluate the: (1) difference in dentoalveolar heights among subjects with different vertical facial patterns; (2) association between the dentoalveolar bone height and other cephalometric variables; (3) effect of sex on dentoalveolar height measurements. Non-growing subjects with skeletal Class I (0°<ANB<4°), Class I canine and molar relationship and complete fully erupted dentition were included in the study. Cephalometric measurements were taken on lateral cephalograms and patients were stratified into hyperdivergent, hypodivergent, and normodivergent groups based on MP/SN angle. A two-way analysis of variance (ANOVA) was used to compare cephalometric measurements between the divergence and sex groups. Measurements on a total of 204 patients (81 males, 123 females) were analysed. At the level of the incisor and premolar regions, both dentoalveolar and alveolar bone heights were significantly larger in hyperdivergent group when compared to hypodivergent group, whereas at the level of the molar region, no difference was found among groups. Moderate to strong correlations were found between dentoalveolar bone and vertical skeletal measurements. In the maxilla and mandible, the dentoalveolar compensation mechanism in skeletal Class I subjects results in an increased vertical height in the anterior dentoalveolar segment in hyperdivergent subjects and a reduced one in hypodivergent subjects.

Evaluation of Buccolingual Inclinations of First Molars Among Different Vertical Facial Patterns in Class II Division I Subjects.

Background Knowing the characteristics of vertical patterns is crucial to provide the best orthodontic treatment. Cone beam computed tomography (CBCT) offers a valuable tool for evaluating true buccolingual inclinations. The current study investigates the buccolingual inclination of first molars in adult subjects with different vertical facial patterns. Methods CBCT scans of 66 adult patients (31 males, 35 females) with a mean age of 31.6 years (SD: 6.4 years) exhibiting skeletal class II division I maxillomandibular relationships were employed. Participants were categorized into three groups based on linear and angular measurements: normodivergent group (n=22), hypodivergent group (n=22), and hyperdivergent group (n=22). The independent samples t-test and Mann-Whitney U-test were conducted to investigate statistical differences in terms of buccolingual inclination between the three vertical patterns. Results Statistically significant differences were observed in the buccolingual inclinations of both maxillary and mandibular first molars in the hypodivergent group compared to the other groups (p<0.05). Conclusions In patients with class II division I sagittal relationships, the buccolingual inclinations of the first molars exhibit similarities between normodivergent and hyperdivergent groups. However, these inclinations differ significantly in hypodivergent adult subjects.

Mandibular Contouring in Subjects With Class II or Class III Dentofacial Deformities and Treatment Needs

Facial contouring relates to hard and soft structures that make up the face. Skeletal class II and III subjects present bone structure and position changes, significantly impacting the soft tissues. This study aims to analyze the morphology of the mandible at mandibular ramus and angle level in subjects with skeletal facial deformity class II and III who are candidates for orthognathic surgery and to define implications in facial contour. A cross-sectional study used cone beam computed tomography to compare the mandibular contour (mandibular angle and ramus region) in orthognathic surgery candidates. One hundred sixty orthognathic surgery candidates were analyzed, ranging in age from 18 to 58 years (31.29 ± 11.97). Regarding the skeletal class, 95 (59.37%) were skeletal class II, and 65 (40.62%) were skeletal class III. Class II subjects had a larger antegonial notch than class III subjects. Concerning the mandibular contour, class II subjects presented less vertical distance than class III subjects, but both presented similar gonial angles. Concerning the ideas to assess the need for mandibular contouring surgery, the 2 proposals to determine the need for mandibular contouring surgery in class II and III subjects present a similar distribution. The mandibular notch is an objective element and is commonly present in subjects with a class II facial pattern; this element can be used in contour analysis to define expected facial characteristics, including the patient’s facial sex, in the decision of surgical techniques for lower facial contour augmentation or reduction.

Facial soft-tissue shape changes after fixed edgewise treatment with premolar extraction in individual artificial-intelligence-classified facial profile patterns

ObjectiveTo examine the patterns of pretreatment facial soft tissue shape in orthodontic cases with premolar extraction using artificial intelligence (AI) and to investigate the corresponding changes.MethodsOne hundred and fifty-two patients who underwent orthodontic treatment with premolar extraction were enrolled. Lateral cephalograms were obtained before and after the treatment. For each record, the outlines of the nose-lip-chin profile and corresponding 21 cephalometric variables were extracted. The AI method classified pretreatment records into three subject groups based on the feature variables extracted from the outline. Dentoskeletal and soft tissue facial form changes observed after treatment were compared statistically (P < 0.05) between the groups using ANOVA. Multivariate regression models were used for each group.ResultsGroup 1 (n = 59) was characterized by Class II high-angle retrognathic mandible with an incompetent lip, group 2 (n = 55) by Class I malocclusion with retruded and thin lips, and group 3 (n = 38) by Class I malocclusion with an everted superior lip before treatment. The ratios of anteroposterior soft tissue to hard tissue movements in Group 1 were 56% (r = 0.64) and 83% (r = 0.75) for the superior and inferior lips, respectively, whereas those in Group 2 were 49% (r = 0.78) and 91% (r = 0.80), and 40% (r = 0.54) and 79% (r = 0.70), respectively, in Group 3.ConclusionsThe modes of facial form changes differed depending on the pre-treatment profile patterns classified by the AI. This indicates that the determination of the pre-treatment profile pattern can help in the selection of soft tissue to hard tissue movement ratios, which helps estimate the post-treatment facial profile with a moderate to high correlation.

Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders

BackgroundMinor physical anomalies (MPAs) are congenital morphological abnormalities linked to disruptions of fetal development. MPAs are common in 22q11.2 deletion syndrome (22q11DS) and psychosis spectrum disorders (PS) and likely represent a disruption of early embryologic development that may help identify overlapping mechanisms linked to psychosis in these disorders.MethodsHere, 2D digital photographs were collected from 22q11DS (n = 150), PS (n = 55), and typically developing (TD; n = 93) individuals. Photographs were analyzed using two computer-vision techniques: (1) DeepGestalt algorithm (Face2Gene (F2G)) technology to identify the presence of genetically mediated facial disorders, and (2) Emotrics—a semi-automated machine learning technique that localizes and measures facial features.ResultsF2G reliably identified patients with 22q11DS; faces of PS patients were matched to several genetic conditions including FragileX and 22q11DS. PCA-derived factor loadings of all F2G scores indicated unique and overlapping facial patterns that were related to both 22q11DS and PS. Regional facial measurements of the eyes and nose were smaller in 22q11DS as compared to TD, while PS showed intermediate measurements.ConclusionsThe extent to which craniofacial dysmorphology 22q11DS and PS overlapping and evident before the impairment or distress of sub-psychotic symptoms may allow us to identify at-risk youths more reliably and at an earlier stage of development.

Driver drowsiness is associated with altered facial thermal patterns: Machine learning insights from a thermal imaging approach

Driver drowsiness is a significant factor in road accidents. Thermal imaging has emerged as an effective tool for detecting drowsiness by enabling the analysis of facial thermal patterns. However, it is not clear which facial areas are most affected and correlate most strongly with drowsiness. This study examines the variations and importance of various facial areas and proposes an approach for detecting driver drowsiness. Twenty participants underwent tests in a driving simulator, and temperature changes in various facial regions were measured. The random forest method was employed to evaluate the importance of each facial region. The results revealed that temperature changes in the nasal area exhibited the highest value, while the eyes had the most correlated changes with drowsiness. Furthermore, drowsiness was classified with an accuracy of 88 % utilizing thermal variations in the facial region identified as the most important regions by the random forest feature importance model. These findings provide a comprehensive overview of facial thermal imaging for detecting driver drowsiness and introduce eye temperature as a novel and effective measure for investigating cognitive activities.

Facial morphological analysis for the development of a representative atlas of the brazilian population

The objective of this study was to propose categories of morphological classification for the face and its anatomical structures, as well as to propose illustrations to support the development of an atlas that facilitates facial morphological analysis of adult Brazilians. It was a descriptive study based on the analysis of the frequency and distribution of 13 photoanthropometric facial ratios (RFAs) obtained from a representative sample of the Brazilian population. RFAs related to facial height and width, eye width, intercanthal distance, nose length and width, philtrum ridge height and width, mouth thickness and width, upper and lower lip thickness, and chin height were analyzed. The study included a sample of 5.000 individuals aged between 18 and 22 years, evenly distributed between genders. Data normality was assessed using the Shapiro-Wilk test, considering them as parametric when p > 0.05. For the RFAs that showed normal distribution, mean ± 1.5 standard deviations (SD) were used to categorize facial measurements as regular, below average, or above average. Non-parametric RFAs were analyzed based on the median and 10th and 90th percentiles of the data. Based on the established average iris diameter, which is considered the most stable facial measurement, the values of the described RFAs were converted to a numerical scale in centimeters, allowing for the illustration of female and male faces. In this way, it was possible to categorize the facial anatomical structures and, consequently, visualize the facial morphological pattern of the adult Brazilian population.

Bifurcation of the mandibular canal according to sex, age, and facial skeletal pattern: a morphological and morphometric study of cone beam computed tomography.

To assess the presence of mandibular canal bifurcation (BMC) and classify these variations by correlating findings with sex, age and facial skeletal pattern, measurements were made, including height, width, and distance from bifurcation to anatomical cortical bones. BMC was identified in cone beam CT exams of 301 patients and classified according to its origin, location, direction, configuration and ending. The height and width of the MC before and after the bifurcation; height and width of the BMC; and distance from BMC to alveolar (C1), buccal (C2), lingual (C3) and basal (C4) bone cortices were measured. All data were correlated with sex, age, and facial skeletal pattern (class I, II, III). The significance level was 5%. 67 BMC (22.26%) were identified in 55 patients (18.28%). Bifurcations were more prevalent in females (p = 0.57), aged 18-39 years (p = 0.40), class I (p = 0.77). Single bifurcations, located in the posterior region of the mandible, originating in the MC, with a superior direction and ending in the retromolar foramen were more prevalent (p > 0.05). Mean cortical measurements were higher in male individuals, with significant differences only at C1 (p = 0.03). The mean height and width of BMC were 2.24 (± 0.62) and 1.75 (± 0.45) mm. There was no association between BMC classification and the variables studied (p > 0.05). Approximately 1/5 of the population studied had BMC. There were no associations of BMC presence or characteristics with sex, age, and facial skeletal pattern. The distance from bifurcation to alveolar (superior) cortical bone is greater in male individuals.