Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particula...Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.展开更多
Given the current expansion of the computer visionfield,several appli-cations that rely on extracting biometric information like facial gender for access control,security or marketing purposes are becoming more common....Given the current expansion of the computer visionfield,several appli-cations that rely on extracting biometric information like facial gender for access control,security or marketing purposes are becoming more common.A typical gender classifier requires many training samples to learn as many distinguishable features as possible.However,collecting facial images from individuals is usually a sensitive task,and it might violate either an individual's privacy or a specific data privacy law.In order to bridge the gap between privacy and the need for many facial images for deep learning training,an artificially generated dataset of facial images is proposed.We acquire a pre-trained Style-Generative Adversar-ial Networks(StyleGAN)generator and use it to create a dataset of facial images.We label the images according to the observed gender using a set of criteria that differentiate the facial features of males and females apart.We use this manually-labelled dataset to train three facial gender classifiers,a custom-designed network,and two pre-trained networks based on the Visual Geometry Group designs(VGG16)and(VGG19).We cross-validate these three classifiers on two separate datasets containing labelled images of actual subjects.For testing,we use the UTKFace and the Kaggle gender dataset.Our experimental results suggest that using a set of artificial images for training produces a comparable performance with accuracies similar to existing state-of-the-art methods,which uses actual images of individuals.The average classification accuracy of each classifier is between 94%and 95%,which is similar to existing proposed methods.展开更多
Myasthenia Gravis(MG)is an autoimmune neuromuscular disease.Given that extraocular muscle manifestations are the initial and primary symptoms in most patients,ocular muscle assessment is regarded necessary early scree...Myasthenia Gravis(MG)is an autoimmune neuromuscular disease.Given that extraocular muscle manifestations are the initial and primary symptoms in most patients,ocular muscle assessment is regarded necessary early screening tool.To overcome the limitations of the manual clinical method,an intuitive idea is to collect data via imaging devices,followed by analysis or processing using Deep Learning(DL)techniques(particularly image segmentation approaches)to enable automatic MG evaluation.Unfortunately,their clinical applications in this field have not been thoroughly explored.To bridge this gap,our study prospectively establishes a new DL-based system to promote the diagnosis of MG disease,with a complete workflow including facial data acquisition,eye region localization,and ocular structure segmentation.Experimental results demonstrate that the proposed system achieves superior segmentation performance of ocular structure.Moreover,it markedly improves the diagnostic accuracy of doctors.In the future,this endeavor can offer highly promising MG monitoring tools for healthcare professionals,patients,and regions with limited medical resources.展开更多
When a facial image is blurred,it significantly affects high-level vision tasks such as face recognition.The purpose of facial image deblurring is to recover a clear image from a blurry input image,which can improve t...When a facial image is blurred,it significantly affects high-level vision tasks such as face recognition.The purpose of facial image deblurring is to recover a clear image from a blurry input image,which can improve the recognition accuracy,etc.However,general deblurring methods do not perform well on facial images.Therefore,some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images.In this paper,we survey and summarize recently published methods for facial image deblurring,most of which are based on deep learning.First,we provide a brief introduction to the modeling of image blurring.Next,we summarize face deblurring methods into two categories:model-based methods and deep learning-based methods.Furthermore,we summarize the datasets,loss functions,and performance evaluation metrics commonly used in the neural network training process.We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods.Finally,we discuss the current challenges and possible future research directions.展开更多
In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fi...In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.展开更多
Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female parti...Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female participants(anemic and healthy)were recruited from Shuguang Hospital.Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System(FDAS)and Tongue Diagnosis Analysis System version 2.0(TDAS v2.0).Least Absolute Shrinkage and Selection Operator(LASSO)regression was used for feature selection.Ten machine learning models and one deep learning model(ResNet50V2+Conv1D)were developed and evaluated.Results Anemic women showed lower a-values,higher L-and b-values across all age groups.Texture features analysis showed that women aged 30–39 with anemia had higher angular second moment(ASM)and lower entropy(ENT)values in facial images,while those aged 40–49 had lower contrast(CON),ENT,and MEAN values in tongue images but higher ASM.Anemic women exhibited age-related trends similar to healthy women,with decreasing L-values and increasing a-,b-,and ASM-values.LASSO identified 19 key features from 62.Among classifiers,the Artificial Neural Network(ANN)model achieved the best performance[area under the curve(AUC):0.849,accuracy:0.781].The ResNet50V2 model achieved comparable results[AUC:0.846,accuracy:0.818].Conclusion Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.展开更多
It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Eur...It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics, Genome-wide associ- ation studies （GWAS） were conducted on a discovery panel of Uyghurs, Six significant loci were iden- tified, four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A prospective model was also developed to predict 3D faces based on top GWAS signals and tested in hypothetic forensic scenarios.展开更多
Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful...Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods.展开更多
Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression a...Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of mind.Normally,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of bipolar.In this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based model.Facial Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry emotions.Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental state.The experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,respectively.Overall,the outcomes demonstrated the stated method’s superiority over the current best practices.展开更多
Deep forgery detection technologies are crucial for image and video recognition tasks,with their performance heavily reliant on the features extracted from both real and fake images.However,most existing methods prima...Deep forgery detection technologies are crucial for image and video recognition tasks,with their performance heavily reliant on the features extracted from both real and fake images.However,most existing methods primarily focus on spatial domain features,which limits their accuracy.To address this limitation,we propose an adaptive dual-domain feature representation method for enhanced deep forgery detection.Specifically,an adaptive region dynamic convolution module is established to efficiently extract facial features from the spatial domain.Then,we introduce an adaptive frequency dynamic filter to capture effective frequency domain features.By fusing both spatial and frequency domain features,our approach significantly improves the accuracy of classifying real and fake facial images.Finally,experimental results on three real-world datasets validate the effectiveness of our dual-domain feature representation method,which substantially improves classification precision.展开更多
A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained Y...A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained You Only Look Once(YOLO)detector,then applies image coordinates of the detected bounding box(bbox).First,the data that is used to train our model is collected under various circumstances such as light disturbances,distances,time variations,and different climate conditions.It also contains various mask types to detect in general and universal application of the model.To detect mask-wearing status,it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images.Furthermore,the Convolutional Neural Network(CNN)model is trained with both our own dataset and open dataset to detect under heavy foot-traffic(Indoors).To make the model robust and reliable in various environment and situations,we collected various sample data in different distances.And through the experiment,we found out that there is a particular gradient according to the mask-wearing status.The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum.Then it carry out the classification of mask-wearing status of detected object.Lastly,we defined and classified three different mask-wearing states according to the mask’s position(With mask,Wear a mask around chin and Without mask).The gradient according to the mask-wearing status,is analyzed through linear regression.The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System.Through the experiments,we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments.And the proposed method,through linear regression analysis,classifies in a very concise way than the others.展开更多
The symptoms of autism spectrum disorder（ASD） have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the‘‘disconnectivity＇＇ hypothesis has been used to explain chara...The symptoms of autism spectrum disorder（ASD） have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the‘‘disconnectivity＇＇ hypothesis has been used to explain characteristic impairments in ‘‘socio-emotional＇＇ function.Therefore, in this study we compared the facial emotional recognition（FER） feature and the integrity of socialemotional-related white-matter tracts between children and adolescents with high-functioning ASD（HFA） and their typically developing（TD） counterparts. The correlation between the two factors was explored to find out if impairment of the white-matter tracts is the neural basis of social-emotional disorders. Compared with the TD group,FER was significantly impaired and the fractional anisotropy value of the right cingulate fasciculus was increased in the HFA group（P ／ 0.01）. In conclusion, the FER function of children and adolescents with HFA was impaired and the microstructure of the cingulate fasciculus had abnormalities.展开更多
In dimensional affect recognition, the machine learning methods, which are used to model and predict affect, are mostly classification and regression. However, the annotation in the dimensional affect space usually ta...In dimensional affect recognition, the machine learning methods, which are used to model and predict affect, are mostly classification and regression. However, the annotation in the dimensional affect space usually takes the form of a continuous real value which has an ordinal property. The aforementioned methods do not focus on taking advantage of this important information. Therefore, we propose an affective rating ranking framework for affect recognition based on face images in the valence and arousal dimensional space. Our approach can appropriately use the ordinal information among affective ratings which are generated by discretizing continuous annotations.Specifically, we first train a series of basic cost-sensitive binary classifiers, each of which uses all samples relabeled according to the comparison results between corresponding ratings and a given rank of a binary classifier. We obtain the final affective ratings by aggregating the outputs of binary classifiers. By comparing the experimental results with the baseline and deep learning based classification and regression methods on the benchmarking database of the AVEC 2015 Challenge and the selected subset of SEMAINE database, we find that our ordinal ranking method is effective in both arousal and valence dimensions.展开更多
Phenotypic diversity,especially that of facial morphology,has not been fully investigated in the Han Chinese,which is the largest ethnic group in the world.In this study,we systematically analyzed a total of 14,838 fa...Phenotypic diversity,especially that of facial morphology,has not been fully investigated in the Han Chinese,which is the largest ethnic group in the world.In this study,we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional(3D)manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals.Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions:Zhengzhou,Taizhou,and Nanning.We identifed 1560 shared features from extracted phenotypes,which characterized well the basic facial morphology of the Han Chinese.In particular,heterogeneous phenotypes showing population structures corresponded to geographical subpopulations.The greatest facial variation among these geographical populations was the angle of glabella,left subalare,and right cheilion(p=3.4×10^(−161)).Interestingly,we found that Han Chinese populations could be classifed into northern Han,central Han,and southern Han at the phenotypic level,and the facial morphological variation pattern of central Han Chinese was between the typical diferentiation of northern and southern Han Chinese.This result was highly consistent with the results revealed by the genetic data.These fndings provide new insights into the analysis of multidimensional phenotypes as well as a valuable resource for further facial phenotype-genotype association studies in Han Chinese and East Asian populations.展开更多
Objective:This study addresses the limitations of existing traditional Chinese medicine(TCM)constitution identification techniques for the elderly by proposing an intelligent identification method aimed at enhancing t...Objective:This study addresses the limitations of existing traditional Chinese medicine(TCM)constitution identification techniques for the elderly by proposing an intelligent identification method aimed at enhancing the accuracy,standardization,and formalization of the identification process.Materials and Methods:Leveraging data from the images of the tongue,face,and pulse,this study introduced four image classification models:EfficientNetV2,MobileViT,Vision Transformer,and Swin Transformer.A comparative experimental approach was employed to establish a baseline model.Subsequently,a multi-information fusion model was constructed on this foundation,extracting integrated features from diverse data to further improve identification accuracy.Results:The multi-information fusion model developed in this study achieved an accuracy of 71.32%,effectively enhancing the accuracy of TCM constitution identification for the elderly.Conclusions:The multi-information fusion model developed in this study,by integrating tongue,facial,and pulse data,considerably enhances the accuracy of TCM constitution identification.It effectively addresses the certain limitations inherent in existing TCM constitution identification techniques,offering a novel and efficacious strategy for this domain.展开更多
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
文摘Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.
文摘Given the current expansion of the computer visionfield,several appli-cations that rely on extracting biometric information like facial gender for access control,security or marketing purposes are becoming more common.A typical gender classifier requires many training samples to learn as many distinguishable features as possible.However,collecting facial images from individuals is usually a sensitive task,and it might violate either an individual's privacy or a specific data privacy law.In order to bridge the gap between privacy and the need for many facial images for deep learning training,an artificially generated dataset of facial images is proposed.We acquire a pre-trained Style-Generative Adversar-ial Networks(StyleGAN)generator and use it to create a dataset of facial images.We label the images according to the observed gender using a set of criteria that differentiate the facial features of males and females apart.We use this manually-labelled dataset to train three facial gender classifiers,a custom-designed network,and two pre-trained networks based on the Visual Geometry Group designs(VGG16)and(VGG19).We cross-validate these three classifiers on two separate datasets containing labelled images of actual subjects.For testing,we use the UTKFace and the Kaggle gender dataset.Our experimental results suggest that using a set of artificial images for training produces a comparable performance with accuracies similar to existing state-of-the-art methods,which uses actual images of individuals.The average classification accuracy of each classifier is between 94%and 95%,which is similar to existing proposed methods.
基金funded by the National High Level Hospital Clinical Research Funding(No.BJ-2023-111).
文摘Myasthenia Gravis(MG)is an autoimmune neuromuscular disease.Given that extraocular muscle manifestations are the initial and primary symptoms in most patients,ocular muscle assessment is regarded necessary early screening tool.To overcome the limitations of the manual clinical method,an intuitive idea is to collect data via imaging devices,followed by analysis or processing using Deep Learning(DL)techniques(particularly image segmentation approaches)to enable automatic MG evaluation.Unfortunately,their clinical applications in this field have not been thoroughly explored.To bridge this gap,our study prospectively establishes a new DL-based system to promote the diagnosis of MG disease,with a complete workflow including facial data acquisition,eye region localization,and ocular structure segmentation.Experimental results demonstrate that the proposed system achieves superior segmentation performance of ocular structure.Moreover,it markedly improves the diagnostic accuracy of doctors.In the future,this endeavor can offer highly promising MG monitoring tools for healthcare professionals,patients,and regions with limited medical resources.
基金We acknowledge the support from the research grants No.E2RC5901 and No.E3KW5902.
文摘When a facial image is blurred,it significantly affects high-level vision tasks such as face recognition.The purpose of facial image deblurring is to recover a clear image from a blurry input image,which can improve the recognition accuracy,etc.However,general deblurring methods do not perform well on facial images.Therefore,some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images.In this paper,we survey and summarize recently published methods for facial image deblurring,most of which are based on deep learning.First,we provide a brief introduction to the modeling of image blurring.Next,we summarize face deblurring methods into two categories:model-based methods and deep learning-based methods.Furthermore,we summarize the datasets,loss functions,and performance evaluation metrics commonly used in the neural network training process.We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods.Finally,we discuss the current challenges and possible future research directions.
文摘In computer vision and artificial intelligence,automatic facial expression-based emotion identification of humans has become a popular research and industry problem.Recent demonstrations and applications in several fields,including computer games,smart homes,expression analysis,gesture recognition,surveillance films,depression therapy,patientmonitoring,anxiety,and others,have brought attention to its significant academic and commercial importance.This study emphasizes research that has only employed facial images for face expression recognition(FER),because facial expressions are a basic way that people communicate meaning to each other.The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency.This review is on machine learning,deep learning,and hybrid methods’use of preprocessing,augmentation techniques,and feature extraction for temporal properties of successive frames of data.The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically.In this review,a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation.The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.
基金Funding This research was funded by funding from the National Natural Science Foundation of China(No.82305090,No.82104738)Key Discipline Construction Project of High-level Traditional Chinese Medicine of the National Administration of Traditional Chinese Medicine-Traditional Chinese Medical Diagnostics(ZYYZDXK-2023069)+5 种基金Shanghai Municipal Health Commission Project(No.20234Y0168,No.2024QN018)Shanghai Science and Technology Commission Rising Star Cultivation Project(No.22YF1448900)Capacity Building of Local Colleges and Universities under the Shanghai Municipal Science and Technology Commission(21010504400)General Program of China Postdoctoral Science Foundation(2023M732337)Shanghai“Super Postdoctoral”Incentive Plan(2022509)Science and Technology Development Project of Shanghai University of Traditional Chinese Medicine(23KFL005).
文摘Objective To identify the key features of facial and tongue images associated with anemia in female populations,establish anemia risk-screening models,and evaluate their performance.Methods A total of 533 female participants(anemic and healthy)were recruited from Shuguang Hospital.Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument.Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System(FDAS)and Tongue Diagnosis Analysis System version 2.0(TDAS v2.0).Least Absolute Shrinkage and Selection Operator(LASSO)regression was used for feature selection.Ten machine learning models and one deep learning model(ResNet50V2+Conv1D)were developed and evaluated.Results Anemic women showed lower a-values,higher L-and b-values across all age groups.Texture features analysis showed that women aged 30–39 with anemia had higher angular second moment(ASM)and lower entropy(ENT)values in facial images,while those aged 40–49 had lower contrast(CON),ENT,and MEAN values in tongue images but higher ASM.Anemic women exhibited age-related trends similar to healthy women,with decreasing L-values and increasing a-,b-,and ASM-values.LASSO identified 19 key features from 62.Among classifiers,the Artificial Neural Network(ANN)model achieved the best performance[area under the curve(AUC):0.849,accuracy:0.781].The ResNet50V2 model achieved comparable results[AUC:0.846,accuracy:0.818].Conclusion Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
基金funded by the Max-Planck-Gesellschaft Partner Group Grant (KT)the National Natural Science Foundation of China (Nos.31371267,31322030,91331108 (KT)+10 种基金91731303,31771388,and 31711530221 (SX)91631307 (SW)31501011 (YL) and 31260263 (YG))supported by Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDB13040100,SXXDB13041000,SW)the National Science Fund for Distinguished Young Scholars (31525014,SX)the Program of Shanghai Academic Research Leader (16XD1404700,to SX)the support of a National Thousand Young Talents Award and a Max Planck-CAS Paul Gerson Unna Independent Research Group Leadership Award (SW)the Science and Technology Commission of Shanghai Municipality (16JC1400504,SW14YF1406800,YL16YF1413900,HL)
文摘It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics, Genome-wide associ- ation studies （GWAS） were conducted on a discovery panel of Uyghurs, Six significant loci were iden- tified, four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A prospective model was also developed to predict 3D faces based on top GWAS signals and tested in hypothetic forensic scenarios.
文摘Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods.
文摘Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of mind.Normally,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of bipolar.In this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based model.Facial Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry emotions.Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental state.The experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,respectively.Overall,the outcomes demonstrated the stated method’s superiority over the current best practices.
基金supported in part by the National Natural Science Foundation of China under No.12401679the Nature Science Foundation of the Jiangsu Higher Education Institutions of China under No.23KJB520006the Haizhou Bay Talent Innovation Program of Jiangsu Ocean University under No.PD2024026。
文摘Deep forgery detection technologies are crucial for image and video recognition tasks,with their performance heavily reliant on the features extracted from both real and fake images.However,most existing methods primarily focus on spatial domain features,which limits their accuracy.To address this limitation,we propose an adaptive dual-domain feature representation method for enhanced deep forgery detection.Specifically,an adaptive region dynamic convolution module is established to efficiently extract facial features from the spatial domain.Then,we introduce an adaptive frequency dynamic filter to capture effective frequency domain features.By fusing both spatial and frequency domain features,our approach significantly improves the accuracy of classifying real and fake facial images.Finally,experimental results on three real-world datasets validate the effectiveness of our dual-domain feature representation method,which substantially improves classification precision.
基金This research was supported by a grant(2019-MOIS32-027)of Regional Specialized Disaster-Safety Research Support Program funded by the Ministry of Interior and Safety(MOIS,Korea)This work was supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01972).
文摘A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained You Only Look Once(YOLO)detector,then applies image coordinates of the detected bounding box(bbox).First,the data that is used to train our model is collected under various circumstances such as light disturbances,distances,time variations,and different climate conditions.It also contains various mask types to detect in general and universal application of the model.To detect mask-wearing status,it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images.Furthermore,the Convolutional Neural Network(CNN)model is trained with both our own dataset and open dataset to detect under heavy foot-traffic(Indoors).To make the model robust and reliable in various environment and situations,we collected various sample data in different distances.And through the experiment,we found out that there is a particular gradient according to the mask-wearing status.The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum.Then it carry out the classification of mask-wearing status of detected object.Lastly,we defined and classified three different mask-wearing states according to the mask’s position(With mask,Wear a mask around chin and Without mask).The gradient according to the mask-wearing status,is analyzed through linear regression.The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System.Through the experiments,we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments.And the proposed method,through linear regression analysis,classifies in a very concise way than the others.
基金supported by The National Key Research and Development Program of China (2016YFC1306200)the National Natural Science Foundation of China (91132750)+1 种基金Major Projects of the National Social Science Foundation of China (14ZDB161)the Key Research and Development Program of Jiangsu Province, China (BE2016616)
文摘The symptoms of autism spectrum disorder（ASD） have been hypothesized to be caused by changes in brain connectivity. From the clinical perspective, the‘‘disconnectivity＇＇ hypothesis has been used to explain characteristic impairments in ‘‘socio-emotional＇＇ function.Therefore, in this study we compared the facial emotional recognition（FER） feature and the integrity of socialemotional-related white-matter tracts between children and adolescents with high-functioning ASD（HFA） and their typically developing（TD） counterparts. The correlation between the two factors was explored to find out if impairment of the white-matter tracts is the neural basis of social-emotional disorders. Compared with the TD group,FER was significantly impaired and the fractional anisotropy value of the right cingulate fasciculus was increased in the HFA group（P ／ 0.01）. In conclusion, the FER function of children and adolescents with HFA was impaired and the microstructure of the cingulate fasciculus had abnormalities.
基金supported by the National Natural Science Foundation of China(Nos.61272211 and 61672267)the Open Project Program of the National Laboratory of Pattern Recognition(No.201700022)+1 种基金the China Postdoctoral Science Foundation(No.2015M570413)and the Innovation Project of Undergraduate Students in Jiangsu University(No.16A235)
文摘In dimensional affect recognition, the machine learning methods, which are used to model and predict affect, are mostly classification and regression. However, the annotation in the dimensional affect space usually takes the form of a continuous real value which has an ordinal property. The aforementioned methods do not focus on taking advantage of this important information. Therefore, we propose an affective rating ranking framework for affect recognition based on face images in the valence and arousal dimensional space. Our approach can appropriately use the ordinal information among affective ratings which are generated by discretizing continuous annotations.Specifically, we first train a series of basic cost-sensitive binary classifiers, each of which uses all samples relabeled according to the comparison results between corresponding ratings and a given rank of a binary classifier. We obtain the final affective ratings by aggregating the outputs of binary classifiers. By comparing the experimental results with the baseline and deep learning based classification and regression methods on the benchmarking database of the AVEC 2015 Challenge and the selected subset of SEMAINE database, we find that our ordinal ranking method is effective in both arousal and valence dimensions.
基金the Basic Science Center Program(32288101)the National Natural Science Foundation of China(NSFC)grants(32271186,31771325,32030020,31961130380,T2122007,and 32070577)the National Science and Technology Basic Research Project(2015FY111700 to LJ).
文摘Phenotypic diversity,especially that of facial morphology,has not been fully investigated in the Han Chinese,which is the largest ethnic group in the world.In this study,we systematically analyzed a total of 14,838 facial traits representing 15 categories with both a large-scale three-dimensional(3D)manual landmarking database and computer-aided facial segmented phenotyping in 2379 Han Chinese individuals.Our results illustrate that homogeneous and heterogeneous facial morphological traits exist among Han Chinese populations across the three geographical regions:Zhengzhou,Taizhou,and Nanning.We identifed 1560 shared features from extracted phenotypes,which characterized well the basic facial morphology of the Han Chinese.In particular,heterogeneous phenotypes showing population structures corresponded to geographical subpopulations.The greatest facial variation among these geographical populations was the angle of glabella,left subalare,and right cheilion(p=3.4×10^(−161)).Interestingly,we found that Han Chinese populations could be classifed into northern Han,central Han,and southern Han at the phenotypic level,and the facial morphological variation pattern of central Han Chinese was between the typical diferentiation of northern and southern Han Chinese.This result was highly consistent with the results revealed by the genetic data.These fndings provide new insights into the analysis of multidimensional phenotypes as well as a valuable resource for further facial phenotype-genotype association studies in Han Chinese and East Asian populations.
文摘Objective:This study addresses the limitations of existing traditional Chinese medicine(TCM)constitution identification techniques for the elderly by proposing an intelligent identification method aimed at enhancing the accuracy,standardization,and formalization of the identification process.Materials and Methods:Leveraging data from the images of the tongue,face,and pulse,this study introduced four image classification models:EfficientNetV2,MobileViT,Vision Transformer,and Swin Transformer.A comparative experimental approach was employed to establish a baseline model.Subsequently,a multi-information fusion model was constructed on this foundation,extracting integrated features from diverse data to further improve identification accuracy.Results:The multi-information fusion model developed in this study achieved an accuracy of 71.32%,effectively enhancing the accuracy of TCM constitution identification for the elderly.Conclusions:The multi-information fusion model developed in this study,by integrating tongue,facial,and pulse data,considerably enhances the accuracy of TCM constitution identification.It effectively addresses the certain limitations inherent in existing TCM constitution identification techniques,offering a novel and efficacious strategy for this domain.
