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1.
J Forensic Odontostomatol ; 1(40): 34-41, 2022 Apr 30.
Artigo em Inglês | MEDLINE | ID: mdl-35499535

RESUMO

The aim of this study was to collect soft tissue thickness (STT) values of an Italian population from 12 bone landmarks, to improve the facial approximation process for identification purposes. 100 Italian adults (50 males and 50 females), who had undergone head CT for clinical purposes, were analysed in order to expand the database of the Italian population. Average values, standard deviation and range were collected according to gender and age and the obtained values were statistically analysed in order to evaluate any possible significant difference. Only one landmark was statistically significant associated with sex, females showed significantly higher values for para-zygomaxillary. Two landmarks were statistically significant associated with age, upper incisor and pogonion. The obtained results were compared with the existing literature. Such information can be useful in the forensic craniofacial reconstruction process and can facilitate choosing the most suitable STT values according to osteological analysis of the human remains.


Assuntos
Reconhecimento Facial Automatizado , Antropologia Forense , Adulto , Face/anatomia & histologia , Face/diagnóstico por imagem , Feminino , Antropologia Forense/métodos , Humanos , Masculino , Tomografia Computadorizada por Raios X
2.
Comput Math Methods Med ; 2022: 5137513, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35190751

RESUMO

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.


Assuntos
Reconhecimento Facial Automatizado , Aprendizado Profundo , Internet das Coisas , Algoritmos , COVID-19 , Segurança Computacional , Simulação por Computador , Bases de Dados Factuais , Desenho de Equipamento , Humanos , Reconhecimento Automatizado de Padrão , SARS-CoV-2 , Máquina de Vetores de Suporte
3.
Ann Otol Rhinol Laryngol ; 131(4): 373-378, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-34109843

RESUMO

OBJECTIVE: Microtia reconstruction is technically challenging due to the intricate contours of the ear. It is common practice to use a two-dimensional tracing of the patient's normal ear as a template for the reconstruction of the affected side. Recent advances in three-dimensional (3D) surface scanning and printing have expanded the ability to create surgical models preoperatively. This study aims to describe a simple and affordable process to fabricate patient-specific 3D ear models for use in the operating room. STUDY DESIGN: Applied basic research on a novel 3D optical scanning and fabrication pathway for microtia reconstruction. SETTING: Tertiary care university hospital. METHODS: Optical surface scanning of the patient's normal ear was completed using a smartphone with facial recognition capability. The Heges application used the phone's camera to capture the 3D image. The 3D model was digitally isolated and mirrored using the Meshmixer software and printed with a 3D printer (MonopriceTM Select Mini V2) using polylactic acid filaments. RESULTS: The 3D model of the ear served as a helpful intraoperative reference and an adjunct to the traditional 2D template. Collectively, time for imaging acquisition, editing, and fabrication was approximately 3.5 hours. The upfront cost was around $210, and the recurring cost was approximately $0.35 per ear model. CONCLUSION: A novel, low-cost approach to fabricate customized 3D models of the ear is introduced. It is feasible to create individualized 3D models using currently available consumer technology. The low barrier to entry raises the possibility for clinicians to incorporate 3D printing into various clinical applications.


Assuntos
Microtia Congênita/patologia , Microtia Congênita/cirurgia , Modelos Anatômicos , Modelagem Computacional Específica para o Paciente , Procedimentos Cirúrgicos Reconstrutivos , Smartphone , Reconhecimento Facial Automatizado , Microtia Congênita/diagnóstico por imagem , Humanos , Imageamento Tridimensional , Poliésteres , Impressão Tridimensional
4.
Comput Math Methods Med ; 2021: 7748350, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34824599

RESUMO

The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of face detection would be limited because of the missing and false detection of small faces, due to image quality, face scale, light, and other factors. In this paper, a two-level face detection model called SR-YOLOv5 is proposed to address some problems of dense small faces in actual scenarios. The research first optimized the backbone and loss function of YOLOv5, which is aimed at achieving better performance in terms of mean average precision (mAP) and speed. Then, to improve face detection in blurred scenes or low-resolution situations, we integrated image superresolution technology on the detection head. In addition, some representative deep-learning algorithm based on face detection is discussed by grouping them into a few major categories, and the popular face detection benchmarks are enumerated in detail. Finally, the wider face dataset is used to train and test the SR-YOLOv5 model. Compared with multitask convolutional neural network (MTCNN), Contextual Multi-Scale Region-based CNN (CMS-RCNN), Finding Tiny Faces (HR), Single Shot Scale-invariant Face Detector (S3FD), and TinaFace algorithms, it is verified that the proposed model has higher detection precision, which is 0.7%, 0.6%, and 2.9% higher than the top one. SR-YOLOv5 can effectively use face information to accurately detect hard-to-detect face targets in complex scenes.


Assuntos
Algoritmos , Reconhecimento Facial Automatizado/métodos , Face/anatomia & histologia , Redes Neurais de Computação , Reconhecimento Facial Automatizado/estatística & dados numéricos , Biologia Computacional , Aprendizado Profundo , Humanos , Medidas de Segurança/estatística & dados numéricos
5.
PLoS One ; 16(10): e0258672, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34665834

RESUMO

The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.


Assuntos
Reconhecimento Facial Automatizado/métodos , Orquiectomia/efeitos adversos , Medição da Dor/veterinária , Algoritmos , Animais , Bases de Dados Factuais , Aprendizado Profundo , Reconhecimento Facial , Cavalos , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Orquiectomia/veterinária , Gravação em Vídeo
6.
PLoS One ; 16(10): e0257923, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34648520

RESUMO

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.


Assuntos
Reconhecimento Facial Automatizado/métodos , Pesquisa Biomédica/métodos , Gerenciamento de Dados/métodos , Atenção à Saúde/métodos , Reconhecimento Facial , Disseminação de Informação/métodos , Opinião Pública , Adolescente , Adulto , Idoso , Feminino , Humanos , Masculino , Registros Médicos , Pessoa de Meia-Idade , Privacidade , Inquéritos e Questionários , Estados Unidos , Adulto Jovem
7.
PLoS One ; 16(10): e0258241, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34644306

RESUMO

Automatic facial recognition technology (AFR) is increasingly used in criminal justice systems around the world, yet to date there has not been an international survey of public attitudes toward its use. In Study 1, we ran focus groups in the UK, Australia and China (countries at different stages of adopting AFR) and in Study 2 we collected data from over 3,000 participants in the UK, Australia and the USA using a questionnaire investigating attitudes towards AFR use in criminal justice systems. Our results showed that although overall participants were aligned in their attitudes and reasoning behind them, there were some key differences across countries. People in the USA were more accepting of tracking citizens, more accepting of private companies' use of AFR, and less trusting of the police using AFR than people in the UK and Australia. Our results showed that support for the use of AFR depends greatly on what the technology is used for and who it is used by. We recommend vendors and users do more to explain AFR use, including details around accuracy and data protection. We also recommend that governments should set legal boundaries around the use of AFR in investigative and criminal justice settings.


Assuntos
Atitude , Reconhecimento Facial Automatizado , Direito Penal , Opinião Pública , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Inquéritos e Questionários , Confiança , Adulto Jovem
8.
IEEE Trans Image Process ; 30: 7636-7648, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34469297

RESUMO

Convolutional neural networks are capable of extracting powerful representations for face recognition. However, they tend to suffer from poor generalization due to imbalanced data distributions where a small number of classes are over-represented (e.g. frontal or non-occluded faces) and some of the remaining rarely appear (e.g. profile or heavily occluded faces). This is the reason why the performance is dramatically degraded in minority classes. For example, this issue is serious for recognizing masked faces in the scenario of ongoing pandemic of the COVID-19. In this work, we propose an Attention Augmented Network, called AAN-Face, to handle this issue. First, an attention erasing (AE) scheme is proposed to randomly erase units in attention maps. This well prepares models towards occlusions or pose variations. Second, an attention center loss (ACL) is proposed to learn a center for each attention map, so that the same attention map focuses on the same facial part. Consequently, discriminative facial regions are emphasized, while useless or noisy ones are suppressed. Third, the AE and the ACL are incorporated to form the AAN-Face. Since the discriminative parts are randomly removed by the AE, the ACL is encouraged to learn different attention centers, leading to the localization of diverse and complementary facial parts. Comprehensive experiments on various test datasets, especially on masked faces, demonstrate that our AAN-Face models outperform the state-of-the-art methods, showing the importance and effectiveness.


Assuntos
Reconhecimento Facial Automatizado/métodos , Face/anatomia & histologia , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , COVID-19 , Humanos , Máscaras
9.
J Alzheimers Dis ; 83(1): 57-63, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34250937

RESUMO

BACKGROUND: Possible benefits of makeup therapy, in terms of immediate and late effects on cognitive and affective functions, have not been fully proved for dementia patients. OBJECTIVE: To evaluate the immediate effect of makeup therapy on dementia patients. METHODS: Female nursing home residents with dementia received either only skin care treatment (control group, n = 17) or skin care plus makeup therapy treatment (makeup therapy group, n = 19). Cognitive, affective, and activity of daily living (ADL) scores were evaluated before and just after treatments. Apparent age and emotion were also evaluated with artificial intelligence (AI) software. RESULTS: Makeup therapy significantly improved Abe's behavioral and psychological symptoms of dementia (BPSD) score (ABS, *p < 0.05). AI software judged that makeup therapy significantly made the apparent age younger (*p < 0.05). In particular, patients with moderate ADL scores had a significantly higher happiness score in makeup therapy (*p < 0.05), with a modest correlation to the Mini-Mental State Examination (MMSE, r = 0.42, *p < 0.05). The severe baseline MMSE group reported a greater feeling of satisfaction following makeup therapy (*p < 0.05). CONCLUSION: The present makeup therapy is a promising non-pharmacological approach to immediately alleviate BPSD in female dementia patients, and the present AI software quickly and quantitatively evaluated the beneficial effects of makeup therapy on facial appearance.


Assuntos
Inteligência Artificial , Reconhecimento Facial Automatizado , Beleza , Sintomas Comportamentais , Demência/terapia , Higiene da Pele , Atividades Cotidianas/psicologia , Idoso de 80 Anos ou mais , Demência/psicologia , Feminino , Humanos , Testes de Estado Mental e Demência/estatística & dados numéricos , Casas de Saúde , Satisfação do Paciente , Escalas de Graduação Psiquiátrica/estatística & dados numéricos , Software
10.
PLoS One ; 16(7): e0254965, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34293012

RESUMO

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.


Assuntos
Algoritmos , Reconhecimento Facial Automatizado , Interpretação de Imagem Assistida por Computador , Reconhecimento Automatizado de Padrão , Software , Humanos , Análise de Componente Principal
11.
Eur J Med Genet ; 64(9): 104267, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34161860

RESUMO

Down syndrome is one of the most common chromosomal anomalies affecting the world's population, with an estimated frequency of 1 in 700 live births. Despite its relatively high prevalence, diagnostic rates based on clinical features have remained under 70% for most of the developed world and even lower in countries with limited resources. While genetic and cytogenetic confirmation greatly increases the diagnostic rate, such resources are often non-existent in many low- and middle-income countries, particularly in Sub-Saharan Africa. To address the needs of countries with limited resources, the implementation of mobile, user-friendly and affordable technologies that aid in diagnosis would greatly increase the odds of success for a child born with a genetic condition. Given that the Democratic Republic of the Congo is estimated to have one of the highest rates of birth defects in the world, our team sought to determine if smartphone-based facial analysis technology could accurately detect Down syndrome in individuals of Congolese descent. Prior to technology training, we confirmed the presence of trisomy 21 using low-cost genomic applications that do not need advanced expertise to utilize and are available in many low-resourced countries. Our software technology trained on 132 Congolese subjects had a significantly improved performance (91.67% accuracy, 95.45% sensitivity, 87.88% specificity) when compared to previous technology trained on individuals who are not of Congolese origin (p < 5%). In addition, we provide the list of most discriminative facial features of Down syndrome and their ranges in the Congolese population. Collectively, our technology provides low-cost and accurate diagnosis of Down syndrome in the local population.


Assuntos
Reconhecimento Facial Automatizado/métodos , Síndrome de Down/patologia , Facies , Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Facial Automatizado/economia , Reconhecimento Facial Automatizado/normas , República Democrática do Congo , Países em Desenvolvimento , Síndrome de Down/genética , Testes Genéticos , Humanos , Processamento de Imagem Assistida por Computador/economia , Processamento de Imagem Assistida por Computador/normas , Aprendizado de Máquina , Sensibilidade e Especificidade
12.
Plast Reconstr Surg ; 148(1): 45-54, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34181603

RESUMO

BACKGROUND: Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery. METHODS: Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system-ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery. In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction. RESULTS: The neural network preoperative age accuracy score demonstrated that all four neural networks were accurate in identifying ages (mean score, 100.8). Patient self-appraisal age reduction reported a greater age reduction than neural network age reduction after a face lift (-6.7 years versus -4.3 years). FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (75.1 ± 8.1), quality of life (82.4 ± 8.3), and satisfaction with outcome (79.0 ± 6.3). Finally, there was a positive correlation between neural network age reduction and patient satisfaction. CONCLUSION: Artificial intelligence algorithms can reliably estimate the reduction in apparent age after face-lift surgery; this estimated age reduction correlates with patient satisfaction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.


Assuntos
Reconhecimento Facial Automatizado/estatística & dados numéricos , Aprendizado Profundo/estatística & dados numéricos , Satisfação do Paciente/estatística & dados numéricos , Rejuvenescimento , Ritidoplastia/estatística & dados numéricos , Idoso , Reconhecimento Facial Automatizado/métodos , Face/diagnóstico por imagem , Face/cirurgia , Estudos de Viabilidade , Feminino , Seguimentos , Humanos , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Pessoa de Meia-Idade , Medidas de Resultados Relatados pelo Paciente , Período Pós-Operatório , Período Pré-Operatório , Qualidade de Vida , Reprodutibilidade dos Testes , Resultado do Tratamento
13.
Plast Reconstr Surg ; 148(1): 162-169, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34181613

RESUMO

BACKGROUND: Despite the wide range of cleft lip morphology, consistent scales to categorize preoperative severity do not exist. Machine learning has been used to increase accuracy and efficiency in detection and rating of multiple conditions, yet it has not been applied to cleft disease. The authors tested a machine learning approach to automatically detect and measure facial landmarks and assign severity grades using preoperative photographs. METHODS: Preoperative images were collected from 800 unilateral cleft lip patients, manually annotated for cleft-specific landmarks, and rated using a previously validated severity scale by eight expert reviewers. Five convolutional neural network models were trained for landmark detection and severity grade assignment. Mean squared error loss and Pearson correlation coefficient for cleft width ratio, nostril width ratio, and severity grade assignment were calculated. RESULTS: All five models performed well in landmark detection and severity grade assignment, with the largest and most complex model, Residual Network, performing best (mean squared error, 24.41; cleft width ratio correlation, 0.943; nostril width ratio correlation, 0.879; severity correlation, 0.892). The mobile device-compatible network, MobileNet, also showed a high degree of accuracy (mean squared error, 36.66; cleft width ratio correlation, 0.901; nostril width ratio correlation, 0.705; severity correlation, 0.860). CONCLUSIONS: Machine learning models demonstrate the ability to accurately measure facial features and assign severity grades according to validated scales. Such models hold promise for the creation of a simple, automated approach to classifying cleft lip morphology. Further potential exists for a mobile telephone-based application to provide real-time feedback to improve clinical decision making and patient counseling.


Assuntos
Fenda Labial/diagnóstico , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Nariz/anormalidades , Índice de Gravidade de Doença , Pontos de Referência Anatômicos , Reconhecimento Facial Automatizado/métodos , Fenda Labial/complicações , Fenda Labial/cirurgia , Tomada de Decisão Clínica , Aconselhamento , Conjuntos de Dados como Assunto , Estudos de Viabilidade , Humanos , Aplicativos Móveis , Nariz/diagnóstico por imagem , Nariz/cirurgia , Fotografação , Período Pré-Operatório , Consulta Remota , Rinoplastia
14.
IEEE Trans Image Process ; 30: 5313-5326, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34038362

RESUMO

In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose. Specifically, we leverage a landmark-graph relational network to enforce the structural relationships among landmarks. We consider the facial landmarks as structural graph nodes and carefully design the neighborhood to passing features among the most related nodes. Our method dynamically adapts the weights of node neighborhood to eliminate distracted information from noisy nodes, such as occluded landmark point. Moreover, different from most previous works which only tend to penalize the landmarks absolute position during the training, we propose a relative location loss to enhance the information of relative location of landmarks. This relative location supervision further regularizes the facial structure. Our approach considers the interactions among facial landmarks and can be easily implemented on top of any convolutional backbone to boost the performance. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method. In particular, due to explicit structure modeling, our approach is especially robust to challenging cases resulting in impressive low failure rate on COFW and WFLW datasets. The model and code are publicly available at https://github.com/BeierZhu/Sturcture-Coherency-Face-Alignment.


Assuntos
Reconhecimento Facial Automatizado/métodos , Aprendizado Profundo , Face/anatomia & histologia , Pontos de Referência Anatômicos/anatomia & histologia , Bases de Dados Factuais , Humanos
15.
Plast Surg Nurs ; 41(2): 112-116, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34033638

RESUMO

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.


Assuntos
Reconhecimento Facial Automatizado/instrumentação , Nível de Saúde , Avaliação em Enfermagem/métodos , Inteligência Artificial/tendências , Reconhecimento Facial Automatizado/métodos , Humanos , Avaliação em Enfermagem/normas
17.
J Am Med Inform Assoc ; 28(7): 1548-1554, 2021 Jul 14.
Artigo em Inglês | MEDLINE | ID: mdl-33713140

RESUMO

OBJECTIVE: Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. MATERIALS AND METHODS: For these reasons, in this article, we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the MobileNetV2 model to identify face mask violations in images/video streams. Moreover, the proposed approach is able to localize the area related to the face mask detection with relative probability. RESULTS: To asses the effectiveness of the proposed approach, we evaluate a dataset composed of 4095 images related to people wearing and not wearing face masks, obtaining an accuracy of 0.98 in face mask detection. DISCUSSION AND CONCLUSION: The experimental analysis shows that the proposed method can be successfully exploited for face mask violation detection. Moreover, we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal applicable in the real world.


Assuntos
Reconhecimento Facial Automatizado , COVID-19 , Aprendizado Profundo , Máscaras , Conjuntos de Dados como Assunto , Feminino , Humanos , Masculino
18.
Biomed Res Int ; 2021: 6696357, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33778081

RESUMO

BACKGROUND: Sedentary lifestyle and work from home schedules due to the ongoing COVID-19 pandemic in 2020 have caused a significant rise in obesity across adults. With limited visits to the doctors during this period to avoid possible infections, there is currently no way to measure or track obesity. METHODS: We reviewed the literature on relationships between obesity and facial features, in white, black, hispanic-latino, and Korean populations and validated them against a cohort of Indian participants (n = 106). The body mass index (BMI) and waist-to-hip ratio (WHR) were obtained using anthropometric measurements, and body fat mass (BFM), percentage body fat (PBF), and visceral fat area (VFA) were measured using body composition analysis. Facial pictures were also collected and processed to characterize facial geometry. Regression analysis was conducted to determine correlations between body fat parameters and facial model parameters. RESULTS: Lower facial geometry was highly correlated with BMI (R 2 = 0.77) followed by PBF (R 2 = 0.72), VFA (R 2 = 0.65), WHR (R 2 = 0.60), BFM (R 2 = 0.59), and weight (R 2 = 0.54). CONCLUSIONS: The ability to predict obesity using facial images through mobile application or telemedicine can help with early diagnosis and timely medical intervention for people with obesity during the pandemic.


Assuntos
Antropometria/métodos , Reconhecimento Facial Automatizado/métodos , COVID-19/epidemiologia , Obesidade/diagnóstico , Adulto , Composição Corporal , Índice de Massa Corporal , Peso Corporal , Reconhecimento Facial/fisiologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Obesidade/epidemiologia , Obesidade/metabolismo , Pandemias , Valor Preditivo dos Testes , Prognóstico , Fatores de Risco , SARS-CoV-2/isolamento & purificação , Circunferência da Cintura , Relação Cintura-Quadril
19.
J Surg Res ; 260: 377-382, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33750544

RESUMO

BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. Furthermore, when surveyed, patients express a desire for their care provider to mirror their own race and ethnicity. In the present study, we hypothesize that there is a positive correlation between a high ranking by the US News and World Report for gastroenterology and gastrointestinal (GI) surgery and greater racial, ethnic, and gender diversity among the physicians and surgeons. METHODS: We used the 2019 US News and World Report rankings for best hospitals by specialty to categorize gastroenterology and GI surgery departments into 2 groups: 1-50 and 51-100. Hospital websites of these top 100 were viewed to determine if racial diversity and inclusion were highlighted in the hospitals' core values or mission statements. To determine the rates of diversity within departments, Betaface (Betaface.com) facial analysis software was used to analyze photos taken from the hospitals' websites. This software was able to determine the race, ethnicity, and gender of the care providers. We examined the racial and ethnic makeup of the populations served by these hospitals to see if the gastroenterologists and surgeons adequately represented the state population. We then ran the independent samples t-test to determine if there was a difference in rankings of more diverse departments. RESULTS: Hospitals with gastroenterology and GI surgery departments in the top 50 were more likely to mention diversity on their websites compared with hospitals that ranked from 51-100 (76% versus 56%; P = 0.035). The top 50 hospitals had a statistically significant higher percentage of underrepresented minority GI physicians and surgeons (7.01% versus 4.04%; P < 0.001). In the 31 states where these hospitals were located, there were more African Americans (13% versus 3%; P < 0.001) and Hispanics (12% versus 2%; P < 0.001), while there were fewer Asians (4% versus 21%; P < 0.001) in the population compared with the faculty. CONCLUSIONS: We used artificial intelligence software to determine the degree of racial and ethnic diversity in gastroenterology and GI surgery departments across the county. Higher ranking hospitals had a greater degree of diversity of their faculty and were more likely to emphasize diversity in their mission statements. Hospitals stress the importance of having a culturally diverse staff, yet their care providers may not adequately reflect the populations they serve. Further work is needed to prospectively track diversity rates over time and correlate these changes with measurable outcomes.


Assuntos
Inteligência Artificial , Reconhecimento Facial Automatizado , Diversidade Cultural , Gastroenterologia/normas , Grupos Minoritários/estatística & dados numéricos , Garantia da Qualidade dos Cuidados de Saúde/métodos , /estatística & dados numéricos , Feminino , Gastroenterologia/organização & administração , Gastroenterologia/estatística & dados numéricos , Equidade de Gênero , Departamentos Hospitalares/organização & administração , Departamentos Hospitalares/normas , Departamentos Hospitalares/estatística & dados numéricos , Humanos , Masculino , Avaliação de Resultados em Cuidados de Saúde , Satisfação do Paciente/estatística & dados numéricos , Indicadores de Qualidade em Assistência à Saúde/estatística & dados numéricos , Estados Unidos
20.
Mol Genet Genomic Med ; 9(5): e1636, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33773094

RESUMO

INTRODUCTION: Patients with Noonan and Williams-Beuren syndrome present similar facial phenotypes modulated by their ethnic background. Although distinctive facial features have been reported, studies show a variable incidence of those characteristics in populations with diverse ancestry. Hence, a differential diagnosis based on reported facial features can be challenging. Although accurate diagnoses are possible with genetic testing, they are not available in developing and remote regions. METHODS: We used a facial analysis technology to identify the most discriminative facial metrics between 286 patients with Noonan and 161 with Williams-Beuren syndrome with diverse ethnic background. We quantified the most discriminative metrics, and their ranges both globally and in different ethnic groups. We also created population-based appearance images that are useful not only as clinical references but also for training purposes. Finally, we trained both global and ethnic-specific machine learning models with previous metrics to distinguish between patients with Noonan and Williams-Beuren syndromes. RESULTS: We obtained a classification accuracy of 85.68% in the global population evaluated using cross-validation, which improved to 90.38% when we adapted the facial metrics to the ethnicity of the patients (p = 0.024). CONCLUSION: Our facial analysis provided for the first time quantitative reference facial metrics for the differential diagnosis Noonan and Williams-Beuren syndromes in diverse populations.


Assuntos
Reconhecimento Facial Automatizado/métodos , Diagnóstico por Computador/métodos , Face/patologia , Síndrome de Noonan/diagnóstico , Fenótipo , Síndrome de Williams/diagnóstico , Adolescente , Adulto , Reconhecimento Facial Automatizado/normas , Criança , Pré-Escolar , Diagnóstico por Computador/normas , Diagnóstico Diferencial , Feminino , Humanos , Lactente , Aprendizado de Máquina , Masculino , Sensibilidade e Especificidade
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