A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

Global-local combined features to detect pain intensity from facial expression images with attention mechanism

The estimation of pain intensity is critical for medical diagnosis and treatment of patients.With the development of image monitoring technology and artificial intelligence,automatic pain assessment based on facial expression and behavioral analysis shows a potential value in clinical applications.This paper reports a framework of convolutional neural network with global and local attention mechanism(GLA-CNN)for the effective detection of pain intensity at four-level thresholds using facial expression images.GLA-CNN includes two modules,namely global attention network(GANet)and local attention network(LANet).LANet is responsible for extracting representative local patch features of faces,while GANet extracts whole facial features to compensate for the ignored correlative features between patches.In the end,the global correlational and local subtle features are fused for the final estimation of pain intensity.Experiments under the UNBC-McMaster Shoulder Pain database demonstrate that GLA-CNN outperforms other state-of-the-art methods.Additionally,a visualization analysis is conducted to present the feature map of GLA-CNN,intuitively showing that it can extract not only local pain features but also global correlative facial ones.Our study demonstrates that pain assessment based on facial expression is a non-invasive and feasible method,and can be employed as an auxiliary pain assessment tool in clinical practice.

Re-Distributing Facial Features for Engagement Prediction with ModernTCN

Automatically detecting learners’engagement levels helps to develop more effective online teaching and assessment programs,allowing teachers to provide timely feedback and make personalized adjustments based on students’needs to enhance teaching effectiveness.Traditional approaches mainly rely on single-frame multimodal facial spatial information,neglecting temporal emotional and behavioural features,with accuracy affected by significant pose variations.Additionally,convolutional padding can erode feature maps,affecting feature extraction’s representational capacity.To address these issues,we propose a hybrid neural network architecture,the redistributing facial features and temporal convolutional network(RefEIP).This network consists of three key components:first,utilizing the spatial attention mechanism large kernel attention(LKA)to automatically capture local patches and mitigate the effects of pose variations;second,employing the feature organization and weight distribution(FOWD)module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps.Finally,we analyse the temporal changes in video frames through the modern temporal convolutional network(ModernTCN)module to detect engagement levels.We constructed a near-infrared engagement video dataset(NEVD)to better validate the efficiency of the RefEIP network.Through extensive experiments and in-depth studies,we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation(DAiSEE),achieving an accuracy of 90.8%on NEVD and 61.2%on DAiSEE in the fourclass classification task,indicating significant advantages in addressing engagement video analysis problems.

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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.

Recurrent herpes infection showing a new facial phenotype:A case report

BACKGROUND Facial herpes is a common form of the herpes simplex virus-1 infection and usually presents as vesicles near the mouth,nose,and periocular sites.In contrast,we observed a new facial symptom of herpes on the entire face without vesicles.CASE SUMMARY A 33-year-old woman with a history of varicella infection and shingles since an early age presented with sarcoidosis of the entire face and neuralgia without oral lesions.The patient was prescribed antiviral treatment with valacyclovir and acyclovir cream.One day after drug administration,facial skin lesions and neurological pain improved.Herpes simplex without oral blisters can easily be misdiagnosed as pimples upon visual examination in an outpatient clinic.CONCLUSION As acute herpes simplex is accompanied by neuralgia,prompt diagnosis and prescription are necessary,considering the pathological history and health conditions.

Evaluation of Ear and Facial Indices of Ibibio and Efik Female Children of Akwa Ibom and Cross River States, Nigeria

Background: The ear and face are indispensable and distinctive features for hearing and identification. Objectives: This study was designed to generate anthropometric data of the ear and facial indices of females of Efik and Ibibio children in Cross River and Akwa Ibom States, show morphological and aesthetic differences and ethnicity. Methods: A total of 600 female children (300 Efiks and 300 Ibibios) aged 2 to 10 years that met the inclusion criteria were chosen from selected primary schools in Calabar Municipality, Calabar South of Cross River State and from Uyo, Itu of Akwa Ibom State, Nigeria. Standardized measurements of face length, face width, ear length, and ear width were taken with a spreading caliper;the facial (proscopic) and ear (auricular) indices were determined. Results: Efik subjects presented a mean face length of 8.36 ± 0.06 cm, face width of 11.04 ± 0.04 cm, ear length of 4.92 ± 0.02 cm, and ear width of 3.06 ± 0.01 cm. Ibibio subjects had mean values for face length, face width, ear length, and ear width as 8.17 ± 0.05 cm, 10.75 ± 0.05 cm, 4.77 ± 0.03 cm, and 2.94 ± 0.02 cm respectively. The mean facial index and ear index for Efik subjects were 75.68 ± 0.31 and 62.16 ± 0.27 respectively;while the mean facial and ear indices for Ibibio subjects were 74.79 ± 0.36 and 61.80 ± 0.34 respectively. Statistical analysis demonstrated significant differences in face length, ear length, ear width and facial index, with the Efik subjects having higher values than Ibibio subjects (p Conclusion: The results showed hypereuryproscopic face as the prevalent face type among females of both ethnic groups, therefore can be of importance in sex, ethnic, and racial differentiation, and in clinical practice, aesthetics and forensic medicine.

Epidemiological and Computed Tomography Aspects of Facial Trauma at Kira Hospital in Bujumbura

Background: Maxillofacial trauma affects young adults more. The injury assessment is difficult to establish in low-income countries because of the imaging means, particularly the scanner, which is poorly available and less financially accessible. The aim of this study is to describe the epidemiological profile and the various tomodensitometric aspects of traumatic lesions of the face in patients received in the Radiology department of Kira Hospital. Patients and methods: This is a descriptive retrospective study involving 104 patients of all ages over a period of 2 years from December 2018 to November 2019 in the medical imaging department of KIRA HOSPITAL. We included in our study any patient having undergone a CT scan of the head and presenting at least one lesion of the facial mass, whether associated with other cranioencephalic lesions. Results: Among the 384 patients received for head trauma, 104 patients (27.1% of cases) presented facial damage. The average age of our patients was 32.02 years with extremes of 8 months and 79 years. In our study, 87 of the patients (83.6%) were male. The road accident was the circumstance in which facial trauma occurred in 79 patients (76% of cases). These injuries were accompanied by at least one bone fracture in 97 patients (93.3%). Patients with fractures of more than 3 facial bones accounted for 40.2% of cases and those with fractures of 2 to 3 bones accounted for 44.6% of cases. The midface was the site of the fracture in 85 patients (87.6% of cases). Orbital wall fractures were noted in 57 patients (58.8% of cases) and the jawbone was the site of a fracture in 50 patients (51.5% of cases). In the vault, the fractures involved the extra-facial frontal bone (36.1% of cases) and temporal bone (18.6% of cases). Cerebral contusion was noted in 41.2% of patients and pneumoencephaly in 15.5% of patients. Extradural hematoma was present in 16 patients and subdural hematoma affected 13 patients. Conclusion: Computed tomography is a diagnostic tool of choice in facial trauma patients. Most of these young patients present with multiple fractures localizing to the mid-level of the face with concomitant involvement of the brain.

Sensory Evaluation of Freeze-Dried Facial Mask and Analysis of User Profile

To thoroughly understand market opportunity of freeze-dried facial mask and deeply get insight of consumers’usage behavior and needs,evaluate sensory feelings of 10 screened commercial freeze-dried facial mask products,group test products according to the differences of sensory attributions via Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC),pick up the representative products.Freeze-dried facial mask users evaluate satisfaction degree of picked up products and participate survey of usage behavior/cognition.Analyze consumer data by AHC to get consumer segmentations and their profile.The test results show that,sensory data and consumer data,which is from consumers test of screened representative products by performing PCA and AHC on sensory data,can be verified mutually.It is helpful to understand the needs of consumer segmentations and reason to buy by combining sensory data and consumer test.

A Deep Learning-Based Automated Approach of Schizophrenia Detection from Facial Micro-Expressions

Schizophrenia is a severe mental illness responsible for many of the world’s disabilities.It significantly impacts human society;thus,rapid,and efficient identification is required.This research aims to diagnose schizophrenia directly from a high-resolution camera,which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked eye.In a clinical study by a team of experts at Bahawal Victoria Hospital(BVH),Bahawalpur,Pakistan,there were 300 people with schizophrenia and 299 healthy subjects.Videos of these participants have been captured and converted into their frames using the OpenFace tool.Additionally,pose,gaze,Action Units(AUs),and land-marked features have been extracted in the Comma Separated Values(CSV)file.Aligned faces have been used to detect schizophrenia by the proposed and the pre-trained Convolutional Neural Network(CNN)models,i.e.,VGG16,Mobile Net,Efficient Net,Google Net,and ResNet50.Moreover,Vision transformer,Swim transformer,big transformer,and vision transformer without attention have also been used to train the models on customized dataset.CSV files have been used to train a model using logistic regression,decision trees,random forest,gradient boosting,and support vector machine classifiers.Moreover,the parameters of the proposed CNN architecture have been optimized using the Particle Swarm Optimization algorithm.The experimental results showed a validation accuracy of 99.6%for the proposed CNN model.The results demonstrated that the reported method is superior to the previous methodologies.The model can be deployed in a real-time environment.

Peripheral Facial Paralysis in People Living with Human Immunodeficiency Virus (HIV)

Introduction: Peripheral facial palsy (PFP) is a frequent reason for ENT consultations. It is a common complication of human immunodeficiency virus (HIV) infection. The aim of this study was to describe the diagnostic and therapeutic aspects and to establish the correlation between PFP and HIV in our context. Patients and Method: This was a retrospective descriptive study conducted in the ENT and CFS department of the HIAOBO, covering the medical records of patients hospitalized for taking a PFP on HIV terrain from January 1, 2016 to December 31, 2020. Results: The study involved 17 patients, 10 men (59%) and 7 women (41%), a sex ratio of 1.4. The average age was 39 years with the extremes of 11 and 69 years. Shopkeepers reported 9 cases (53%). The reason for consultation was facial asymmetry in 11 cases (100%). The delay in consultation during the first week was 82.4%. Clinical signs were unilateral facial asymmetry, the opening of the palpebral fissure and lacrimation. All patients received medical treatment for PFP and HIV. Evolution was favorable, with complete recovery and no sequelae in 82.4% of cases. Surgery was performed in one case. Conclusion: PFPs are common in HIV infection. Diagnosis is clinical and management is multidisciplinary. Progression depends on the length of time taken to treat the disease.

Acute traumatic extratemporal facial nerve injury:A 5-year review

Background:Traumatic facial nerve injury is the second leading cause of facial paralysis,presenting significant challenges such as difficulties with eating,speaking,impaired vision,and loss of facial expression.Understanding these injuries is critical for plastic surgeons to optimize recovery and functional outcomes.This study examined our experience with acute traumatic extratemporal facial nerve injuries,focusing on their characteristics,the frequency of affected nerve branches,and identification using Seckel’s facial danger zones as anatomical landmarks.We also explored the implications of surgical management based on the current literature and our experience.Methods:We reviewed 50 patients with acute traumatic extratemporal facial nerve injuries treated at Hospital General Dr.Manuel Gea Gonzalez in Mexico City from January 2019 to January 2024.The collected data included demographics(age,sex,severity,and time to medical attention),injury mechanism,affected nerve branches using Seckel’s zones,and treatment methods.Results:The majority of the patients were male(82%),with an average age of 2916 years.Sharp trauma from assault was the most common cause(66%).The buccal and frontal branches were most frequently affected,with Seckel zonesⅣandⅡinvolved in 46.86%and 33.33%of cases,respectively.Primary neurorrhaphy was performed in 96%of the patients.Conclusion:The effective management of traumatic facial nerve injuries relies on understanding the characteristics of the injury and using anatomical landmarks for prompt localization.Primary neurorrhaphy is the preferred surgical approach,with nerve grafting being an alternative.Ideally,early intervention within 72 h is crucial for optimal nerve recovery.

Plasma radiofrequency-assisted microsuction for the treatment of facial overfilled syndrome induced by diverse causes in Asians

Background:In Asia,facial overfilled syndrome(FOS)can arise from iatrogenic causes involving the excessive use of filling materials,as well as physiological causes such as adipose tissue hypertrophy type,age-related type,and lacuna type.This study aimed to demonstrate the safety and effectiveness of plasma radiofrequency-assisted microsuction(PRFAMS)for improving the appearance of FOS induced by various causes.Methods:PRFAMS was performed on 84 anatomical regions of 37 female patients(including 12 with physiological causes,6 with hyaluronic acid causes,and 19 with fat causes),aged between 20 and 50 years(mean,35.9 years),who had FOS.Demographic and surgical data were collected retrospectively.Preoperative and postoperative photographs were taken,and satisfaction interviews were conducted at least six months after surgery.Results:All the patients underwent successful surgery under local or intravenous anesthesia.Only four anatomic regions showed noticeable asymmetry postoperatively,requiring a secondary operation.Postoperative skin numbness,muscle paralysis,bruising,and minor contouring irregularities improved significantly within 2–3 weeks of recovery.Additionally,five patients with longer edema periods demonstrated substantial improvement after more than five weeks.No cases of skin necrosis,thermal injury,or other serious complications related to the device or procedure were reported.Ultimately,all patients expressed satisfaction with their outcomes.Conclusion:The PRFAMS technique is a safe and effective method for treating FOS induced by various causes,while minimizing complications and ensuring high patient satisfaction.

Facial reanimation with interposition nerve graft or masseter nerve transfer:a comparative retrospective study

Both interposition nerve grafts and masseter nerve transfers have been successfully used for facial reanimation after irreversible injuries to the cranial portion of the facial nerve.However,no comparative study of these two procedures has yet been reported.In this two-site,twoarm,retrospective case review study,32 patients were included.Of these,17 patients(eight men and nine women,mean age 42.1 years)underwent interposition nerve graft after tumor extirpation or trauma between 2003 and 2006 in the Ear Institute,School of Medicine,Shanghai Jiao Tong University,China,and 15 patients(six men and nine women,mean age 40.6 years)underwent masseter-to-facial nerve transfer after tumor extirpation or trauma between November 2010 and February 2016 in Shanghai Ninth People's Hospital,China.More patients achieved House-Brackmann III recovery after masseter nerve repair than interposition nerve graft repair(15/15 vs.12/17).The mean oral commissure excursion ratio was also higher in patients who underwent masseter nerve transfer than in patients subjected to an interposition nerve graft.These findings suggest that masseter nerve transfer results in strong oral commissure excursion,avoiding obvious synkinesis,while an interposition nerve graft provides better resting symmetry.This study was approved by the Institutional Ethics Committee,Shanghai Ninth People's Hospital,China(approval No.SH9 H-2019-T332-1)on December 12,2019.

PROF.ZHANG ANLI'S EXPERIENCE OF TREATING STUBBORN CASES OF FACIAL PALSY

Based on her own experience of many years＇ clinical practice, Prof. Zhang Anli summarized and created the method in which the shallow insertion at the upper eyelid, acupuncture at the Back-shu points and balanced selection of points together with the modified Setting Mountain on Fire and application of Fire Needle were applied for the treatment of stubborn cases of facial palsy. The therapeutic effect is good.

Prenatal diagnosis of isolated lateral facial cleft by ultrasonography and three-dimensional printing:A case report

BACKGROUND Lateral facial clefts are atypical with a low incidence in the facial cleft spectrum.With the development of ultrasonography(US)prenatal screening,such facial malformations can be detected and diagnosed prenatally rather than at birth.Although three-dimensional US(3DUS)can render the fetus'face via 3D reconstruction,the 3D images are displayed on two-dimensional screens without field depth,which impedes the understanding of untrained individuals.In contrast,a 3D-printed model of the fetus'face helps both parents and doctors develop a more comprehensive understanding of the facial malformation by creating more interactive aspects.Herein,we present an isolated lateral facial cleft case that was diagnosed via US combined with a 3D-printed model.CASE SUMMARY A 31-year-old G2P1 patient presented for routine prenatal screening at the 22nd wk of gestation.The coronal nostril-lip section of two-dimensional US(2DUS)demonstrated that the fetus'bilateral oral commissures were asymmetrical,and left oral commissure was abnormally wide.The left oblique-coronal section showed a cleft at the left oral commissure which extended to the left cheek.The results of 3DUS confirmed the cleft.Furthermore,we created a model of the fetal face using 3D printing technology,which clearly presented facial malformations.The fetus was diagnosed with a left lateral facial cleft,which was categorized as a No.7 facial cleft according to the Tessier facial cleft classification.The parents terminated the pregnancy at the 24th wk of gestation after parental counseling.CONCLUSION In the diagnostic course of the current case,in addition to the traditional application of 2D and 3DUS,we created a 3D-printed model of the fetus,which enhanced diagnostic evidence,benefited the education of junior doctors,improved parental counseling,and had the potential to guide surgical planning.

Artificially Generated Facial Images for Gender Classification Using Deep Learning

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.

Facial Index Based 2D Facial Composite Process for Forensic Investigation in Sri Lanka

The “facial composite” is one of the major fields in the forensic science that helps the criminal investigators to carry out their investigation process. The survey conducted by United States Law Enforcement Agencies confirms that 80% of the law enforcement agencies use computer automated composite systems whereas Sri Lanka is still far behind in the process of facial composite with lot of inefficiencies in the current manual process. Hence this research introduces a novel approach for the manual facial composite process, while eliminating the inefficiencies of the manual procedure in Sri Lanka. In order to overcome this situation, this study introduces an automated image processing based software solution with 2D facial feature templates targeting the Sri Lankan population. Thus, this was the first ever approach that creates the 2D facial feature templates by incorporating both medically defined indexes and relevant aesthetic aspects. Hence, this research study is comprised of two separate analyses on anthropometric indices and facial feature shapes which were carried out targeting the local population. Subsequently, several evaluation techniques were utilized to evaluate this methodology where we obtained an overall success rate as 70.19%. The ultimate goal of this research study is to provide a system to the law enforcement agencies in order to carry out an efficient and effective facial composite process which can lead to increase the success rate of suspect identification.

Facial Expression Recognition of Portuguese using American Data as a Reference

It is unknown if the ability of Portuguese in the identification of NimStim data set,which was created in America to provide facial expressions that could be recognized by untrained people,is(or not)similar to the Americans.To test this hypothesis the performance of Portuguese in the recognition of Happiness,Surprise,Sadness,Fear,Disgust and Anger NimStim facial expressions was compared with the Americans,but no significant differences were found.In both populations the easiest emotion to identify was Happiness while Fear was the most difficult one.However,with exception for Surprise,Portuguese tend to show a lower accuracy rate for all the emotions studied.Results highlighted some cultural differences.