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.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception.

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

[Soft and hard tissue changes of hyperdivergent class â ¡ patients before and after orthodontic extraction treatment].

OBJECTIVE: To investigate the hard and soft tissue changing trend and contributing factors of skeletal class Ⅱ hyperdivergent patients before and after orthodontic camouflage treatment by analyzing the cephalogram and the three dimensional (3D) facial scan data. METHODS: Eighteen skeletal class Ⅱ hyperdivergent adult female patients who finished camouflage orthodontic treatment were selected. Skeletal and dental measurements were carried out with the cephalometric analysis before and after the treatment. 3D facial data before and after orthodontic treatment were acquired and the anatomical landmarks were set after the repositioning and superimposition process. Hard tissue measurement included 17 mea-surement indicators (sella-nasion-subspinale angle, sella-nasion-supramental angle, subspinale-nasion-supramental angle, facial angle, angle of convexity, Frankfort horizontal plane-mandibular plane angle (FH-MP), Y axis angle, sella-nasion plane-mandibular plane angle (MP-SN), pogonion-nasion-supramental distance, upper incisor-nasion-subspinale distance, upper incisor to sella-nasion, lower incisor-nasion-supramental distance, lower incisor-nasion-supramental angle, upper incisor to lower incisor, upper incisor to sella-nasion, lower incisor-mandibular plane angle, and Z angle), and the changes before and after treatment were measured for 11 of them. Twenty soft tissue landmarks (left/right cheekbone, left/right chelion, left/right crista philtra, soft tissue gnathion, left/right gonion, glabella, labrale infe-rius, labrale superius, soft tissue menton, left/right mid-mandibular border, soft tissue pogonion, stomion superius, sublabial, subnasale, and supralabial) and 9 soft tissue indicators (lower lip height, facial convexity, lower vermilion height, mandibular contour, nasolabial angle, philtral length, philtral width, upper lip height, and upper vermilion height) were measured and recorded for treatment changes. Linear-regression analysis and correlation analysis were carried out for analyzing the relationship between hard and soft tissue changes before and after the treatment. RESULTS: Significant differences were noticed for 18 out of the 20 cephalometric measurements and facial measurements before and after the treatment (P < 0.05), which mainly represented the sagittal retraction of lip area after the treatment. Significant vertical displacements were revealed for soft tissue menton after treatment [(1.88±2.61) mm, P < 0.05]. Significant sagittal displacements were revealed for left/right cheilion [(-2.95±1.9) mm, (-2.90±1.92) mm], labrale inferius[(-4.94±1.95) mm], labrale superius[(-3.25±1.44) mm], sublabial [(-3.10±3.5) mm], and subnasale [(-1.23±1.06) mm] after treatment (P < 0.05). An average of 4.10°±2.57° increasement was noticed for Z angle after treatment. High correlation (r>0.7) was noticed for the displacement of menton after treatment with FH-MP, with the rate of -0.183 :1, and MP-SN, with the rate of -0.157 :1. Moderate correlations (0.7≥r>0.4) were noticed for the other measurements with correlations (P < 0.05). CONCLUSION: A certain extent of facial improvements could be achieved with orthodontic camouflage treatment for skeletal class Ⅱ hyperdivergent patients, which were mostly represented by the improvement of sagittal relationship of nose, lips, and chin. Certain correlations were noticed for the hard and soft tissue changes.

Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study.

While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify difficult videolaryngoscopy using a neural network. Baseline characteristics, medical history, bedside examination and seven facial images were included as predictor variables. ResNet-18 was introduced to recognise images and extract features. Different machine learning algorithms were utilised to develop predictive models. A videolaryngoscopy view of Cormack-Lehane grade of 1 or 2 was classified as 'non-difficult', while grade 3 or 4 was classified as 'difficult'. A total of 5849 patients were included, of whom 5335 had non-difficult and 514 had difficult videolaryngoscopy. The facial model (only including facial images) using the Light Gradient Boosting Machine algorithm showed the highest area under the curve (95%CI) of 0.779 (0.733-0.825) with a sensitivity (95%CI) of 0.757 (0.650-0.845) and specificity (95%CI) of 0.721 (0.626-0.794) in the test set. Compared with bedside examination and multivariate scores (El-Ganzouri and Wilson), the facial model had significantly higher predictive performance (p < 0.001). Artificial intelligence-based facial analysis is a feasible technique for predicting difficulty during videolaryngoscopy, and the model developed using neural networks has higher predictive performance than traditional methods.

Deformity, etiology, solution, sequence (DESS): Facial analysis in rhinoplasty.

PURPOSE: Rhinoplasty is amongst the most challenging surgeries to perfect and can take decades. This process begins during residency; however, residents often have limited exposure to rhinoplasty during their training and lack a standardized method for systematically analyzing and formulating a surgical plan. The DESS (Deformity, Etiology, Solution, Sequence) is a novel educational format for residents that serves to increase their pre-operative comfort with the surgical evaluation and intraoperative planning for a rhinoplasty. MATERIALS AND METHODS: A qualitative study performed at a tertiary academic institution with an otolaryngology residency program evaluating three consecutive residency classes comprised of four residents per class. A 9-item questionnaire was distributed to measure change in resident comfort after utilizing the DESS during their facial plastics rotation. Questionnaire responses highlighted resident comfort with facial nasal analysis, identifying deformities, suggesting surgical maneuvers, and synthesizing a comprehensive surgical plan. RESULTS: Ten of the twelve residents surveyed responded. Of those that responded, comfort in facial nasal analysis, identification of common nasal deformities, surgical planning, and development of an overall surgical plan were significantly improved after completion of the facial plastic rotation. These residents largely attributed their success to the systematic educational format, with an average score of 4.8/5.0 (SD 0.42). CONCLUSION: While rhinoplasty is a challenging artform to master, systematic approaches to analysis and operative planning are vital for teaching and guiding residents. Through this novel methodology, residents display significant improvement in their comfort with facial nasal analysis and overall surgical preparation.

A reliable and reproducible method for repositioning 3D images to calculate changes in facial volume after orthognathic surgery.

PURPOSE: An accurate, reproducible method to calculate post-operative facial swelling in patients who have undergone orthognathic surgery is important to evaluate the effects of different therapies and surgical techniques on edema. The purpose of this study was to describe such a method and assess its reliability. MATERIALS AND METHODS: A prospective study of patients undergoing orthognathic surgery was conducted. 3D facial photographs were taken on these patients immediately postoperatively, and again at least 21 days later using the 3DMD face system (3DMD LLC., Atlanta, GA, USA). These were cropped using specific anatomic points and the difference in facial volume between the photographs was calculated. Intra-rater reliability and inter-rater reliability were assessed using the Intraclass Correlation Coefficient (ICC). RESULTS: 30 patients were included in the study for analysis. When the difference in facial swelling was calculated twice by the same rater, the mean difference between the two measurements was 4.0 ± 4.2 mL. When calculated by two separate raters, the mean difference was found to be 5.0 ± 3.8 mL. The ICCs for intra-rater and inter-rater reliability were excellent at 0.979 and 0.981 respectively. CONCLUSION: This method allows for reproducible calculation of post-operative facial swelling and could be useful to evaluate the effects of different therapies used to limit swelling and to track the resolution of swelling. It can also potentially be used as a visual aid for patient counseling during the pre-surgical visits.

Is There a Need for Greater Emphasis on Clinical Facial Analysis than Cephalometrics & 3D Software Solutions in the Diagnosis and Treatment Planning of Patients with Dentofacial Deformities?

Purpose: This study is designed to evaluate the need for a greater emphasis on clinical facial analysis over cephalometrics in the diagnosis and treatment planning of patients with dentofacial deformities. Materials and Method: A predetermined questionnaire study was designed to get the thought process of surgeons and consultants involved in orthognathic surgery from various parts of southern India. Two hundred and twenty-eight maxillofacial consultants were involved in the survey. Demographic information, type of professional practice, preferred tool in the diagnosis & treatment planning: Cephalometrics or 3D software solutions and flaw in the available tools were evaluated. Results: The results of this study revealed that only 36.8% of the consultants felt that cephalometrics is the prime tool and 73.3% of the consultants felt that 3D software solutions were superior to cephalometrics in the diagnosis and treatment planning of patients with dentofacial deformities. However, 46% of the consultants preferred facial analysis as the prime tool with cephalometrics as an adjunct. Pertaining to the clinical outcome of their treated cases of dentofacial deformities, 61.8% of the consultants felt the need to address additional cosmetic issues following an orthognathic procedure. It was observed that 92.1% of the participants felt the need for greater emphasis on clinical facial analysis than cephalometrics. Conclusion: Human faces should always be evaluated taking into consideration the various esthetic units of the face. Performing corrective jaw surgery merely based on cephalometric values inevitably fails to address the various other innate imbalances of the face. Hence, cephalometric data should only be considered as an adjunct to clinical judgment in the diagnosis and treatment planning of dentofacial deformities.

Measurement of neuropsychiatric symptoms in the older adults with mild cognitive impairment based on speech and facial expressions: a cross-sectional observational study.

OBJECTIVES: To examine the association between speech and facial features with depression, anxiety, and apathy in older adults with mild cognitive impairment (MCI). METHODS: Speech and facial expressions of 319 MCI patients were digitally recorded via audio and video recording software. Three of the most common neuropsychiatric symptoms (NPS) were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial features were extracted using the open-source data analysis toolkits. Machine learning techniques were used to validate the diagnostic power of extracted features. RESULTS: Different speech and facial features were associated with specific NPS. Depression was associated with spectral and temporal features, anxiety and apathy with frequency, energy, spectral, and temporal features. Additionally, depression was associated with facial features (action unit, AU) 10, 12, 15, 17, 25, anxiety with AU 10, 15, 17, 25, 26, 45, and apathy with AU 5, 26, 45. Significant differences in speech and facial features were observed between males and females. Based on machine learning models, the highest accuracy for detecting depression, anxiety, and apathy reached 95.8%, 96.1%, and 83.3% for males, and 87.8%, 88.2%, and 88.6% for females, respectively. CONCLUSION: Depression, anxiety, and apathy were characterized by distinct speech and facial features. The machine learning model developed in this study demonstrated good classification in detecting depression, anxiety, and apathy. A combination of audio and video may provide objective methods for the precise classification of these symptoms.

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease.

Background and Objective: An aging society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson's disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients' quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerized analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. Methods: We enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrized in the fields of phonation, articulation and prosody. Video recordings of a face were analyzed in terms of facial landmarks movement. Both modalities were consequently modeled by the XGBoost algorithm. Results: The acoustic analysis enabled diagnosis of PD with 77% balanced accuracy, while in the case of the facial analysis, we observed 81% balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83% (88% sensitivity and 78% specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. Conclusions: The introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83%. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Additionally, the clinical interpretation of the created models is illustrated. The presented computer-supported methodology could serve as an extra tool for neurologists in PD detection and the proposed potential solution of mHealth will facilitate the patient's and doctor's life.

"Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm.

Introduction: Multiple tools have been developed for facial feature measurements and analysis using facial recognition machine learning techniques. However, several challenges remain before these will be useful in the clinical context for reconstructive and aesthetic plastic surgery. Smartphone-based applications utilizing open-access machine learning tools can be rapidly developed, deployed, and tested for use in clinical settings. This research compares a smartphone-based facial recognition algorithm to direct and digital measurement performance for use in facial analysis. Methods: Facekit is a camera application developed for Android that utilizes ML Kit, an open-access computer vision Application Programing Interface developed by Google. Using the facial landmark module, we measured 4 facial proportions in 15 healthy subjects and compared them to direct surface and digital measurements using intraclass correlation (ICC) and Pearson correlation. Results: Measurement of the naso-facial proportion achieved the highest ICC of 0.321, where ICC > 0.75 is considered an excellent agreement between methods. Repeated measures analysis of variance of proportion measurements between ML Kit, direct and digital methods, were significantly different (F[2,14] = 6-26, P<<.05). Facekit measurements of orbital, orbitonasal, naso-oral, and naso-facial ratios had overall low correlation and agreement to both direct and digital measurements (R<<0.5, ICC<<0.75). Conclusion: Facekit is a smartphone camera application for rapid facial feature analysis. Agreement between Facekit's machine learning measurements and direct and digital measurements was low. We conclude that the chosen pretrained facial recognition software is not accurate enough for conducting a clinically useful facial analysis. Custom models trained on accurate and clinically relevant landmarks may provide better performance.

Introduction : Il existe de multiples outils pour procéder aux mesures et à l'analyse des caractéristiques faciales à l'aide des techniques d'apprentissage machine de la reconnaissance faciale. Cependant, il reste plusieurs défis à relever avant qu'ils soient utiles en contexte clinique de chirurgie reconstructive et de chirurgie plastique. Des applications pour téléphone intelligent faisant appel à des outils d'apprentissage machine en libre accès peuvent être rapidement mises au point, déployées et mises à l'essai dans un cadre clinique. Dans la présente étude, les chercheurs comparent un algorithme de reconnaissance faciale sur téléphone intelligent pour effectuer les mesures directes et numériques nécessaires lors de l'analyse faciale. Méthodologie : Facekit est une application pour appareil photo de téléphone Android qui fait appel à ML Kit, une application de vision par ordinateur en libre accès créée par Google. Au moyen du module de repères faciaux, les chercheurs ont mesuré quatre proportions faciales chez 15 sujets en santé et les ont comparées aux mesures de surface directe et aux mesures numériques à l'aide de la corrélation intraclasse et de la corrélation de Pearson. Résultats : La mesure de la proportion nasofaciale a obtenu le coefficient de corrélation intraclasse (CCI) le plus élevé, à 0,321, où un CCI supérieur à 0,75 est considéré comme une excellente corrélation entre les méthodes. Des analyses de variance répétées des mesures de proportion entre le ML Kit, la méthode directe et la méthode numérique différaient considérablement (F[2,14] = 6 à 26, p<<0,05). Les mesures Facekit des ratios entre les mesures orbitale, orbitonasale, naso-orale et naso-faciale avaient une faible corrélation globale et étaient corrélées avec les mesures directes et numériques (R<<0,5, CCI<<0,75). Conclusion : Facekit est une application pour appareil photo de téléphone intelligent visant à analyser rapidement les caractéristiques faciales. La concordance entre les mesures d'apprentissage machine de Facekit et les mesures directes et numériques était faible. Les chercheurs concluent que le logiciel de reconnaissance faciale préentraîné n'est pas assez précis pour procéder à une analyse faciale utile sur le plan clinique. Des modèles personnalisés formés à des repères précis et pertinents sur le plan clinique donneront peut-être un meilleur rendement.

Evaluation of craniofacial anthropometry of children with ß-thalassemia major in the eastern Anatolia: a case-control study.

OBJECTIVE: The present study aims to compare ß-thalassemia major patients and healthy individuals in terms of anthropometric characteristics and changes in craniofacial profile. SUBJECTS AND METHOD: Craniofacial anthropometric measurements were performed on a total of 422 subjects (199 ß-thalassemia major patients and 223 healthy individuals) by using a millimetric caliper and tape measure on 19 anthropometric parameters (8 horizontal, 10 vertical, and 1 head circumference) in cranial, facial, nasal, orolabial, and orbital zones. RESULTS: The difference between the orbital, nasal, and orolabial zone parameters of healthy subjects and ß-thalassemia major patients was found to be statistically significant (p < 0.05). There was no statistically significant difference between the groups in terms of head circumference in the cranial zone and total facial height in facial zone (n-gn) values (p˃0.05). In intragroup comparison between females and males with ß-thalassemia, statistically significant differences were found in forehead width (ft-ft), forehead height (tr-gl), right eye width (R ex-ex), and upper lip height (sn-stm) (p < 0.05). CONCLUSION: Understanding the craniofacial profile changes in ß-thalassemia major patients and increasing our knowledge about the relationship between the course and severity of disease and the level of these changes would contribute to the advancements in diagnoses to be made in facial and jaw zones of these patients and in the treatment plans. CLINICAL RELEVANCE: We believe that the analysis and results of the craniofacial anthropometric data obtained in the study will contribute to the diagnosis and treatment processes of patients with ß-thalassemia major in areas of expertise such as craniofacial surgery, orthodontics, and hemato-oncology.

Computational facial analysis for rare Mendelian disorders.

With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.

Application of facial analysis Technology in Clinical Genetics: Considerations for diverse populations.

Facial analysis technology in rare diseases has the potential to shorten the diagnostic odyssey by providing physicians with a valuable diagnostic tool. Given that most clinical genetic resources focus on populations of European descent, we compare craniofacial features in genetic syndromes across different populations and review how machine learning algorithms perform on diagnosing genetic syndromes in geographically and ethnically diverse populations. We also discuss the value of populations from ancestrally diverse backgrounds in the training set of machine learning algorithms. Finally, this review demonstrates that across diverse population groups, machine learning models have outstanding accuracy as supported by the area under the curve values greater than 0.9. Artificial intelligence is only in its infancy in the diagnosis of rare disease in diverse populations and will become more accurate as larger and more diverse training sets, including a wider spectrum of ages, particularly infants, are studied.

Developing a machine learning model for detecting depression, anxiety, and apathy in older adults with mild cognitive impairment using speech and facial expressions: A cross-sectional observational study.

BACKGROUND: Depression, anxiety, and apathy are highly prevalent in older people with preclinical dementia and mild cognitive impairment. These symptoms have also proven valuable in predicting the progression from mild cognitive impairment to dementia, enabling a timely diagnosis and treatment. However, objective and reliable indicators to detect and distinguish depression, anxiety, and apathy are relatively scarce. OBJECTIVE: This study aimed to develop a machine learning model to detect and distinguish depression, anxiety, and apathy based on speech and facial expressions. DESIGN: An observational, cross-sectional study design. SETTING(S): The memory outpatient department of a tertiary hospital. PARTICIPANTS: 319 older adults diagnosed with mild cognitive impairment. METHODS: Depression, anxiety, and apathy were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial expressions of older adults with mild cognitive impairment were digitally captured using audio and video recording software. Open-source data analysis toolkits were utilized to extract speech, facial, and text features. The multiclass classification was used to develop classification models, and shapely additive explanations were used to explain the contribution of each feature within the model. RESULTS: The random forest method was used to develop a multiclass emotion classification model, which performed well in classifying emotions with a weighted-average F1 score of 96.6 %. The model also demonstrated high accuracy, precision, and recall, with 87.4 %, 86.6 %, and 87.6 %, respectively. CONCLUSIONS: The machine learning model developed in this study demonstrated strong classification performance in detecting and differentiating depression, anxiety, and apathy. This innovative approach combines text, audio, and video to provide objective methods for precise classification and remote monitoring of these symptoms in nursing practice. REGISTRATION: This study was registered at the Chinese Clinical Trial Registry (registration number: ChiCTR1900023892; registration date: June 19th, 2019).

Facial Anthropometric Analysis of a Representative Subset in an Average Kerala Population: A Pilot Study.

Background The Neoclassical canons, originally framed based on the Renaissance artworks, vary across genders, races, and ages. This has been proved in multiple studies conducted on the Western population, but minimal studies exist on the Eastern population and lesser so on the Indian population. This study aims to define the standard Keralite face and assess its variation from the canons. Methods A total of 250 people of Kerala origin aged 18 to 40 years were studied over a period of 1 year in our institute. Standardized frontal and profile photographs of the subjects were taken. Twenty anthropometric measurements were taken and analyzed for variation between genders, from published Indian standards and their conformity to the Neoclassical canons. Results Compared to the Keralite men, there were significant differences in 14 of 19 measurements in Keralite women. The men had wider and longer faces than women. Five of 10 measurements in females and 6 of 10 measurements in males significantly differed from the published Indian norms. The average Keralite face was wider, longer, and rounder. None of the facial proportions fit the Neoclassical canons. Conclusion The average Keralite face significantly differed from the Neoclassical canons and there were some significant variations between genders. This study highlights the need for a larger population-based study with more representation from various regions across India.

Facial Analysis for Gender Affirmation/Gender-Related Facial Analysis.

There are anthropometric differences between the bony and integumentary facial features of male and female individuals. When compared to males, female faces in general are more heart-shaped, with a shorter and smoother forehead, a smaller more defined nose, and a tapered chin.

Is facial geometric morphometrics a useful method for age estimation in children and adolescents? Limited evidence and lack of studies leave us with an uncertain answer.

Facial geometric morphometrics is a non-invasive method that has recently shown potential applications, including age estimation, diagnosis of facial abnormalities, monitoring facial development, and evaluating treatment outcomes. A systematic review identified two studies that demonstrated the use of facial geometric morphometrics for age estimation in children and adolescents, showing promising results in terms of accuracy and error. This finding could be particularly relevant in forensic investigations. However, a research agenda should be established to prioritize the assessment of the diagnostic accuracy of facial morphometric geometrics in estimating age among children and adolescents.

Computer-aided facial analysis as a tool to identify patients with Silver-Russell syndrome and Prader-Willi syndrome.

Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver-Russell syndrome (SRS) and Prader-Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1-15 years). CONCLUSION: In addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS. WHAT IS KNOWN: • Several genetic syndromes present typical facial features that may provide clues for the diagnosis. • Memorizing all syndromic facial characteristics is a challenging task for clinicians. WHAT IS NEW: • Face2Gene may represent a useful support for pediatricians for the diagnosis of genetic syndromes. • Face2Gene app can be a useful tool to integrate in the diagnostic path of patients with SRS and PWS.

Clients' Facial Expressions of Self-Compassion, Self-Criticism, and Self-Protection in Emotion-Focused Therapy Videos.

Clients' facial expressions allow psychotherapists to gather more information about clients' emotional processing. This study aims to examine and investigate the facial Action Units (AUs) of self-compassion, self-criticism, and self-protection within real Emotion-Focused Therapy (EFT) sessions. For this purpose, we used the facial analysis software iMotions. Twelve video sessions were selected for the analysis based on specific criteria. For self-compassion, the following AUs were significant: AUs 4 (brow furrow), 15 (lip corner depressor), and the AU12_smile (lip corner puller). For self-criticism, iMotions identified the AUs 2 (outer brow raise), AU1 (inner brow raise), AU7 (lid tighten), AU12_smirk (unilateral lip corner puller), and AU43 (eye closure). Self-protection was combined using the occurrence of AUs 1 and 4 and AU12_smirk. Moreover, the findings support the significance of discerning self-compassion and self-protection as two different concepts.

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis.

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).