Changes In Facial Expression Research Articles

Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders.

The recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring ofneurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss thepotential of AI techniques, including deep learning andcomputer vision, to accurately interpret and categorize subtle changes in facial expressions associated with thesepathological conditions. Furthermore, we explore theroleof facial expression recognition as a noninvasive, cost-effective tool for screening, disease progression tracking, and personalized intervention in neurodegenerative disorders. The review also addresses the challenges, ethical considerations, and future prospects of integrating AI-based facial expression analysis into clinical practice forearly intervention and improved quality of life for individuals at risk ofor affected by neurodegenerative diseases.

Preparation and Rheological Evaluation of Thiol-Maleimide/Thiol-Thiol Double Self-Crosslinking Hyaluronic Acid-Based Hydrogels as Dermal Fillers for Aesthetic Medicine.

This study presents the development of thiol-maleimide/thiol-thiol double self-crosslinking hyaluronic acid-based (dscHA) hydrogels for use as dermal fillers. Hyaluronic acid with varying degrees of maleimide substitution (10%, 20%, and 30%) was synthesized and characterized, and dscHA hydrogels were fabricated using two molecular weights of four-arm polyethylene glycol (PEG10K/20K)-thiol as crosslinkers. The six resulting dscHA hydrogels demonstrated solid-like behavior with distinct physical and rheological properties. SEM analysis revealed a decrease in porosity with higher crosslinker MW and maleimide substitution. The swelling ratios of the six hydrogels reached equilibrium at approximately 1 h and ranged from 20% to 35%, indicating relatively low swelling. Degradation rates decreased with increasing maleimide substitution, while crosslinker MW had little effect. Higher maleimide substitution also required greater injection force. Elastic modulus (G') in the linear viscoelastic region increased with maleimide substitution and crosslinker MW, indicating enhanced firmness. All hydrogels displayed similar creep-recovery behavior, showing instantaneous deformation under constant stress. Alternate-step strain tests indicated that all six dscHA hydrogels could maintain elasticity, allowing them to integrate with the surrounding tissue via viscous deformation caused by the stress exerted by changes in facial expression. Ultimately, the connection between the clinical performance of the obtained dscHA hydrogels used as dermal filler and their physicochemical and rheological properties was discussed to aid clinicians in the selection of the most appropriate hydrogel for facial rejuvenation. While these findings are promising, further studies are required to assess irritation, toxicity, and in vivo degradation before clinical use. Overall, it was concluded that all six dscHA hydrogels show promise as dermal fillers for various facial regions.

Enhanced neural sensitivity to brief changes of happy over angry facial expressions in preschoolers: A fast periodic visual stimulation study.

Across childhood, emotion perception from facial expressions has traditionally been studied with event-related potentials (ERP). Here, we explored the novel fast periodic visual stimulation (FPVS) electroencephalography (EEG) approach to provide information about how brief changes in facial expressions are processed implicitly in young children's brains. Utilizing two FPVS tasks for the first time in preschoolers, we examined brain responses to (1) the discrimination of brief changes in facial expressions at maximum intensity and (2) thresholds for discrimination of gradual increasing facial expression intensities. Within a stream of neutral faces at 6 Hz, happy and angry faces were embedded with a frequency of 1.2 Hz. Additionally, children performed an emotion recognition task (ERT). Data were collected in the context of a training study for socio-emotional competencies with typically developing children (N = 74; 5.1[0.9] years; 34 females). FPVS data were collected post-training, where training was included as a controlling factor. Across FPVS tasks, we detected robust expression change responses, particularly with larger responses for happy versus angry faces in the maximum intensity task. ERT results paralleled neural findings with faster reaction times and higher accuracy rates for happy versus angry faces. For gradual increases in emotional intensity, we found linear increases in responses across emotions. The majority of the sample showed a significant expression change at 60% intensity. With its implicit nature, short duration, and robustness of individual responses, our results highlight the potential of FPVS in comparison to classical ERP methods to study neural mechanisms of emotion perception in preschool samples.

High willingness-to-taste in UK children aged 4–7 years was driven by contextual facilitation, not intervention, in a randomized controlled trial of sensory food education

Many children would benefit from a diet richer in vegetables and fruit. Reluctance to taste new foods is widely recognized as a barrier to healthy dietary variety in children. ‘Flavour School’ is a programme of ‘sensory food education’, aiming to increase children's confidence and curiosity in exploring foods and flavours, especially vegetables and fruit. We report a pre-registered, cluster-randomised controlled trial to assess the outcomes of the Flavour School programme, in n = 160 children aged 4–7 years from 5 UK schools in London and Leeds. Children either received the Flavour School programme (experimental group n = 84) or no intervention (control group n = 76), in a two-level design with children clustered within school classes. Data collection consisted of video recorded behavioural observation during a tasting activity designed to resemble a Flavour School activity, conducted before and after the intervention. Linear regression models were used to fit relationships between independent variables and outcomes; Willingness-to-taste, Enjoyment (positive/negative facial expression), and Expressiveness (change in facial expression). Most children engaged readily in the tasting activity at baseline. At follow-up children engaged with the tasting activity even more readily (on average biting one more food sample), with no significant effect of the intervention. We interpret these findings to indicate that intra-individual tasting behavior in children is context-dependent. Our results suggest that the context of sensory food education activities facilitates openness to tasting across the normal range of individual traits. However, we find no evidence that receiving sensory food education makes a reluctant taster into a less reluctant taster; no ‘curative’ effect of the intervention was observed. Rather, even usually fussy/reluctant children were often willing to have a go at tasting in the conducive context of an exploratory activity. Trial registrationISRCTN: 40249947 Date assigned 17/03/2020 Last edited 24/10/2022 Version 1.2 Trial Acronym OASES (Outcomes Assessment of Sensory Education in Schools)

Assessing 3D volumetric asymmetry in facial palsy patients via advanced multi-view landmarks and radial curves

The research on facial palsy, a unilateral palsy of the facial nerve, is a complex field with many different causes and symptoms. Even modern approaches to evaluate the facial palsy state rely mainly on stills and 2D videos of the face and rarely on dynamic 3D information. Many of these analysis and visualization methods require manual intervention, which is time-consuming and error-prone. Moreover, they often depend on alignment algorithms or Euclidean measurements and consider only static facial expressions. Volumetric changes by muscle movement are essential for facial palsy analysis but require manual extraction. We propose to extract an estimated unilateral volumetric description for dynamic expressions from 3D scans. Accurate landmark positioning is required for processing the unstructured facial scans. In our case, it is attained via a multi-view method compatible with any existing 2D predictors. We analyze prediction stability and robustness against head rotation during video sequences. Further, we investigate volume changes in static and dynamic facial expressions for 34 patients with unilateral facial palsy and visualize volumetric disparities on the face surface. In a case study, we observe a decrease in the volumetric difference between the face sides during happy expressions at the beginning (13.8 ± 10.0 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^{3}$$\\end{document}) and end (12.8 ± 10.3 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^{3}$$\\end{document}) of a ten-day biofeedback therapy. The neutral face kept a consistent volume range of 11.8-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}12.1 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^3$$\\end{document}. The reduced volumetric difference after therapy indicates less facial asymmetry during movement, which can be used to monitor and guide treatment decisions. Our approach minimizes human intervention, simplifying the clinical routine and interaction with 3D scans to provide a more comprehensive analysis of facial palsy.

사전학습 모델 기반 발화 동영상 멀티 모달 감정 인식

Recently, as the demand for non-face-to-face counseling has rapidly increased, the need for emotion recognition technology that combines various aspects such as text, voice, and facial expressions is being emphasized. In this paper, we address issues such as the dominance of non-Korean data and the imbalance of emotion labels in existing datasets like FER-2013, CK+, and AFEW by using Korean video data. We propose methods to enhance multimodal emotion recognition performance in videos by integrating the strengths of image modality with text modality. A pre-trained model is used to overcome the limitations caused by small training data. A GPT-4-based LLM model is applied to text, and a pre-trained model based on VGG-19 architecture is fine-tuned to facial expression images. The method of extracting representative emotions by combining the emotional results of each aspect extracted using a pre-trained model is as follows. Emotion information extracted from text was combined with facial expression changes in a video. If there was a sentiment mismatch between the text and the image, we applied a threshold that prioritized the text-based sentiment if it was deemed trustworthy. Additionally, as a result of adjusting representative emotions using emotion distribution information for each frame, performance was improved by 19% based on F1-Score compared to the existing method that used average emotion values for each frame.

Deep Learning Model to Analyze Psychological Effects on Gamers

The rapid growth of the gaming industry has sparked interest in understanding the psychological effects of games on gamers. This paper explores using deep learning models to analyze the psychological effects experienced by gamers. With video games' increasing popularity and influence, understanding their impact on human psychology is paramount. This research aims to develop a deep learning model that can detect the state of human facial expressions and classify various psychological states based on gameplay data. The proposed deep learning model utilizes Convolutional neural networks to extract meaningful features and patterns from gameplay data. The model is trained on a diverse dataset of gameplay recordings, capturing a wide range of game genres, player demographics, and psychological experiences. The evaluation and validation of the model are performed using metrics, including accuracy, precision, recall, and F1 score. The findings reveal that the deep learning model is effective in detecting and classifying psychological states experienced by gamers. The model successfully captures changes in human facial expressions, such as sadness, happiness, fear, anger, surprise, and disgust, providing valuable insights into the psychological impact of gaming. The results highlight the importance of considering individual differences and contextual factors when examining the influence of games on human psychology. We can advance our knowledge and promote healthy and meaningful gaming experiences by continuing to investigate this complex relationship

Different Effects of Attachment Anxiety and Avoidance on Attention to Changing Facial Expressions

AbstractAttachment orientation has been reported to influence visual attention and cognitive decisions regarding facial expressions. However, previous studies have reported mixed results regarding the effects of attachment orientation. This study aims to examine how attachment anxiety and avoidance affect judgment and attention allocation during free viewing of dynamic facial expressions. Although we did not find any association between attachment orientation and the reaction time to judge the timings of changes in facial expressions, the relationship between attachment orientation and attention was found. When viewing the appearance of anger, attachment avoidance had a positive relationship and attachment anxiety had a negative relationship with looking time to the eye area. When the eye‐tracking data were divided into 12 consecutive intervals of 1 s each, the attachment orientation had different relationships with visual attention based on the type of emotion presented. We discuss possible explanations for these results and directions for further research in cross‐cultural contexts.

Emotion recognition using cross-modal attention from EEG and facial expression

The fusion of facial expression and Electroencephalogram (EEG) signals in a multi-modal framework is a comprehensive and accurate method for emotion recognition. However, current methods often tend to directly concatenate two modalities, thereby overlooking the interplay between modalities. Furthermore, the selection of single static facial images for extracting facial features neglected the dynamic changes in facial expressions. These limitations have been identified as factors that constrain the accuracy and stability of the model. In this study, an innovative multimodal emotion recognition network was introduced, which using continuous facial expressions and EEG signals. It incorporated the cross-modal attention fusion mechanism to establish robust correlations between modal feature vectors, thereby generating amalgamated vectors with mutual information. Additionally, a Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) was employed to capture spatiotemporal information from facial expression images. The model proposed in this paper was experimentally evaluated on the DEAP and MAHNOB-HCI datasets. Its recognition accuracy was higher than the existing advanced research methods. At the same time, it still performed well in the Leave-One-Subject-Out (LOSO) experiment based on DEAP dataset. The experimental results showed the effectiveness of the proposed model in the task of multimodal emotion recognition.

Face swapping in seizure videos for patient deidentification

ObjectiveThis study aimed to test different AI-based face-swapping models applied to videos of epileptic seizures, with the goal of protecting patient privacy while retaining clinically useful seizure semiology. We hypothesized that specific models would show differences in semiologic fidelity compared to the original clinical videos. MethodsThree open-source models, SimSwap, MobileFaceSwap and GHOST were adopted for face-swapping. For every model, an AI generated male and female image were used to replace the original faces. One representative seizure per patient from three patients with epilepsy was chosen (3 seizure videos x 3 AI models x 2 M/F swap) and remade to 18 transformed video clips. To evaluate the performance of the three models, we used both objective (AI-based) and subjective (expert clinician) evaluation. The objective assessment included four metrics for facial appearance and four metrics for facial expression changes. Four experienced epileptologists reviewed the clips and scoring according to deidentification and preservation of semiology. Kruskal-Wallis H test was used for statistical analysis among the models. ResultsIn the reproduced videos, the swapped face cannot be recognized as the original face, with no significant difference in scores of deidentification either by objective or subjective assessment. Regarding semiology preservation, no significant differences between models were observed in the objective evaluations. The subjective evaluations revealed that the GHOST model outperformed the other two models (p=0.028). ConclusionThis is the first study evaluating AI face swapping models in epileptic seizure video clips. Optimization of AI face-swapping models could enhance the accessibility of seizure videos for education and research while protecting patient privacy and maintaining semiology.

Face Recognition Attendance Management System using LBPH and Haar Cascade

This research presents the implementation and design of a facial recognition attendance management system by using Local Binary Pattern Histogram (LBPH) and Haar Cascade machine learning algorithms. This system aims to automate the attendance process, providing an efficient and accurate alternative to traditional methods. The Haar Cascade algorithm is employed for face detection due to its rapid processing and high detection rate, while the LBPH algorithm is utilized for face recognition because it is simple and effective in handling changes in facial expressions, lighting, occlusions, distance from the camera, and camera resolution. The integration of these algorithms results in a robust system capable of real-time attendance tracking. Experimental results demonstrate the system's high accuracy and reliability in face detection and recognition under different conditions of lighting, distance from the camera, face expressions, occlusions, and camera resolution, making it suitable for deployment in educational institutions and corporate environments.

RESEARCH ON THE STATE-OF-THE-ART DEEP LEARNING BASED MODELS FOR FACE DETECTION AND RECOGNITION

The problem of building a face recognition pipeline faces numerous challenges such as changes in lighting, pose, and facial expressions. The main stages of the pipeline include detection, alignment, feature extraction, and face representation. Each of these stages is critically important for achieving accurate recognition. The article analyzes and compares modern algorithms and models for face detection and recognition in terms of their ability to correctly identify true positives (TP) and true negatives (TN) while minimizing false negatives (FN) and false positives (FP) in facial recognition. Classical algorithms and lightweight models, such as MediaPipe, offer the highest speeds but sacrifice some accuracy. Conversely, heavier models like RetinaFace deliver greater accuracy at the expense of speed. For systems prioritizing maximum detection accuracy and minimizing missed faces, models like DSFD or RetinaFace-Resnet50 are recommended, despite their slow performance and unsuitability for real-time detection. If the primary goal is maximum detection speed and occasional missed faces in uncontrolled conditions are acceptable, an SSD face recognition solution is preferable. For applications requiring a balanced approach to speed and accuracy, the RetinaFace-MobilenetV1 model is optimal in terms of real-time detection speed and satisfactory accuracy. The ArcFace model demonstrates superior performance with a TP rate of 0.92 and a TN rate of 0.91, indicating a high accuracy in both identifying the correct person and rejecting mismatched images. ArcFace also maintains a low FP rate of 0.09. FaceNet follows with a TP rate of 0.89 and an impressive TN rate of 0.94, showcasing its proficiency in avoiding incorrect matches. In contrast, VGGFace, DeepFace, and OpenFace show moderate TP rates between 0.61 and 0.78, coupled with higher FN and FP rates. The DeepID model exhibits the lowest performance, with a TP rate of 0.47 and a TN rate of 0.60, reflecting substantial difficulties in accurate identification. The conclusions emphasize the importance of selecting models based on accuracy, speed, and resource requirements, suggesting RetinaFace and ArcFace/FaceNet as good trade-off options.

The investigation of traditional models and machine learning models in dynamic facial expression recognition

In everyday life, dynamic facial expressions are merely continuous human responses to external events. However, in human-computer interaction, rapidly recognizing changes in facial expressions from video streams is a relatively complex process. This complexity renders Dynamic Facial Expression Recognition (DFER) a critical research task in the domains of computer vision and image processing. This paper analyses the correlations and contrasts between static and dynamic facial expression research, highlighting key issues in the study of dynamic facial expressions, such as dynamic feature extraction and frame extraction. After that, it enumerates significant algorithms in both traditional models and deep learning models, providing an analysis of the advantages and disadvantages of these two major approaches. At the same time, it investigates the reasons behind the transition of research models for DFER from traditional methods to deep learning approaches. The paper focuses on two notable models from each approach: Histogram of Oriented Gradient (HOG) for processing raw images, Support Vector Machine (SVM) for data classification in traditional models. Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction in deep learning models. These models are discussed in detail concerning their strengths and weaknesses, operational processes, and performance outcomes. In the concluding section, the author summarizes the main factors influencing research in this field and the current challenges encountered. By focusing on future research directions, the paper also presents a review of recent methodologies and offers insightful research directions for further investigation.

A Real-Time and Privacy-Preserving Facial Expression Recognition System Using an AI-Powered Microcontroller

This study proposes an edge computing-based facial expression recognition system that is low cost, low power, and privacy preserving. It utilizes a minimally obtrusive cap-based system designed for the continuous and real-time monitoring of a user’s facial expressions. The proposed method focuses on detecting facial skin deformations accompanying changes in facial expressions. A multi-zone time-of-flight (ToF) depth sensor VL53L5CX, featuring an 8 × 8 depth image, is integrated into the front brim of the cap to measure the distance between the sensor and the user’s facial skin surface. The distance values corresponding to seven universal facial expressions (neutral, happy, disgust, anger, surprise, fear, and sad) are transmitted to a low-power STM32F476 microcontroller (MCU) as an edge device for data preprocessing and facial expression classification tasks utilizing an on-device pre-trained deep learning model. Performance evaluation of the system is conducted through experiments utilizing data collected from 20 subjects. Four deep learning algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are assessed. These algorithms demonstrate high accuracy, with CNN yielding the best result, achieving an accuracy of 89.20% at a frame rate of 15 frames per second (fps) and a maximum latency of 2 ms.

Age-related changes in cheek skin movement: A case study of Japanese women.

The majority of conventional studies on skin aging have focused on static conditions. However, in daily life, the facial skin we encounter is constantly in motion due to conversational expressions and changes in facial expressions, causing the skin to alter its position and shape, resulting in a dynamic state. Consequently, it is hypothesized that characteristics of aging not apparent in static conditions may be present in the dynamic state of the skin. Therefore, this study investigates age-related changes in dynamic skin characteristics associated with facial expression alterations. A motion capture system measured the dynamic characteristics (delay and stretchiness of skin movement associated with expression) of the cheek skin in response to facial expressions among 86 Japanese women aged between 20 and 69 years. The findings revealed an increase in the delay of cheek skin response to facial expressions (r=0.24, p<0.05) and a decrease in the stretchiness of the lower cheek area with age (r=0.60, p<0.01). An increasing variance in delay and stretchiness within the same age group was also observed with aging. The findings of this study revealed that skin aging encompasses both static characteristics, such as spots, wrinkles, and sagging, traditionally studied in aging research, and dynamic aging characteristics of the skin that emerge in response to facial expression changes. These dynamic aging characteristics could pave the way for the development of new methodologies in skin aging analysis and potentially improve our understanding and treatment of aging impressions that are visually perceptible in daily life but remain unexplored.

Repeated stress triggers seeking of a starvation-like state in anxiety-prone female mice.

Elevated anxiety often precedes anorexia nervosa and persists after weight restoration. Patients with anorexia nervosa often describe self-starvation as pleasant, potentially because food restriction can be anxiolytic. Here, we tested whether repeated stress can cause animals to prefer a starvation-like state. We developed a virtual reality place preference paradigm in which head-fixed mice can voluntarily seek a starvation-like state induced by optogenetic stimulation of hypothalamic agouti-related peptide (AgRP) neurons. Prior to stress exposure, males but not females showed a mild aversion to AgRP stimulation. Strikingly, following multiple days of stress, a subset of females developed a strong preference for AgRP stimulation that was predicted by high baseline anxiety. Such stress-induced changes in preference were reflected in changes in facial expressions during AgRP stimulation. Our study suggests that stress may cause females predisposed to anxiety to seek a starvation state and provides a powerful experimental framework for investigating the underlying neural mechanisms.

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

AbstractThis paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.

On the social signal function of emotional crying: Broadening the perspective to social interactions in daily life.

Emotional crying is a potent signal that can influence how social interactions unfold. Although the social signal function of crying has been mostly studied in experimental approaches involving hypothetical scenarios with strangers as targets, we propose that daily social interactions should be considered to address aspects of external validity. We conducted three retrospective studies (total N = 2,277; convenience samples; Studies 2 and 3 were preregistered; data were collected in 2022 and 2023) and found that individuals primarily observed close others crying in their daily lives; a boundary condition that was hardly reflected in former studies. Crying episodes were typically characterized by tears and changes in facial expression, vocalizations, and gestures, whereas isolated emotional tears, were rarely reported. Participants indicated that they frequently helped close others, but that helping strangers was the exception. The level of familiarity with the target also influenced the form of help provided. Furthermore, the crying person was rated as less warm and less competent when crying (vs. in general or in a neutral situation). Additional analyses suggest that question order effects and recall biases are no plausible alternative explanations of our findings. Taken together, these results call into question the validity of the experimental approaches used in this line of research. We discuss strategies for future study of the social signal function of crying in nonhypothetical scenarios with reference to the "generalizability crisis" of psychological science. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Establishment of Facial Nerve Injury Rat Model for Idiopathic Facial Paralysis Research.

Idiopathic facial paralysis is the most common type of facial nerve injury, accounting for approximately 70% of peripheral facial paralysis cases. This disease can not only lead to a change in facial expression but also greatly impact the psychology of patients. In severe cases, it can affect the normal work and life of patients. Therefore, the research on facial nerve injury repair has important clinical significance. In order to study the mechanism of this disease, it is necessary to carry out relevant animal experiments, among which the most important task is to establish an animal model with the same pathogenesis as human disease. The compression of the facial nerve within the petrous bone, especially the nerve trunk at the junction of the distal end of the internal auditory canal and the labyrinthine segment, is the pathogenesis of idiopathic facial paralysis. In order to simulate this common disease, a compression injury model of the main extracranial segment of the facial nerve was established in this study. The neurological damage was evaluated by behavioral, neuroelectrophysiological, and histological examination. Finally, 50 g constant force and 90 s clamp injury were selected as the injury parameters to construct a stable idiopathic facial paralysis model.

Deteksi Emosi Menggunakan Convolutional Neural Network Berdasarkan Ekspresi Wajah

Facial expression recognition is an effective method for identifying someone's emotional expression. Emotional expressions can be recognized from changes in facial expressions, wrinkles on the forehead, blinking of the eyes, or changes in facial skin color. Facial expressions that a person generally has, such as neutral, angry, happy expressions. The problem that often occurs is the subjective assessment of a person's expression. This research examines how artificial intelligence can recognize facial expressions. The facial recognition process in the research uses a Convolutional Neural Network (CNN), which is a deep learning method capable of carrying out an independent learning process for object recognition, object extraction and classification and can be applied to high resolution images that have a nonparametric distribution model. The two main stages in CNN are feature learning and classification. The results of facial expression recognition can be used to detect a person's emotions. This research uses the FER2013 dataset which contains images of happy, sad, angry, afraid, surprised, disgusted and neutral emotions. The data set in the research received tests that had been carried out, namely the percentage of accuracy level in the model was 76%. It is hoped that the classification of emotions resulting from this research can contribute to the development of artificial intelligence technology and as a tool in various fields such as psychology, education and others. For further research, it can be developed further by adding other architectures such as VGG19, MobileNet, and ResNet-50 so that the resulting CNN model is more optimal.