Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface

People living with Human Immunodeficiency Virus (PLHIV) have been reported to show poor facial emotion recognition. However, these studies presented participants with facial emotion photographs whereas in real life facial emotion recognition hardly involves inferring emotions from static faces. Moreover, emotion recognition from other sensory modalities, such as auditory, has hardly been explored. There’s also a dearth of studies examining emotion regulation difficulties in this group. The present study, thus, explored facial (using facial emotion videos) and auditory emotion recognition as well as difficulties in emotion regulation (using the Hindi version of Difficulties in Emotion Regulation Scale) in 60 PLHIV and 60 people without HIV (PWoHIV). Additionally, the association of HIV duration (duration since diagnosis of HIV), viral load, and Clusters of differentiation 4 (CD4) count with emotion recognition and regulation difficulties in PLHIV was explored. Findings from one-way ANCOVA (with education and socioeconomic status as covariates) revealed significantly impaired auditory emotion recognition (particularly for fear) among PLHIV than PWoHIV. The former also showed significantly poorer facial emotion recognition for surprise. PLHIV also self-reported significantly more emotion regulation difficulties than PWoHIV, specifically Nonacceptance of their response to negative emotions and limited access to emotion regulation Strategies. CD4 count was negatively correlated with emotion regulation difficulties, particularly for accomplishing goal-directed behaviour when experiencing negative emotions (Goals) and Strategies. Besides the novel addition to the literature regarding impaired auditory emotion recognition in PLHIV, these findings can help develop targeted interventions to improve emotion recognition and emotion regulation for PLHIV.

INTRODUCTION: As one of the important research directions in the field of computer vision, facial emotion recognition plays an important role in people's daily life. How to make the computer accurately read facial emotion is an important research content.OBJECTIVES: In the current research on facial emotion recognition, there are some problems such as poor generalization ability of network model and low robustness of recognition system. To solve above problems, we propose a novel facial emotion recognition method.METHODS: Our method of feature extraction using the stationary wavelet entropy, which combines single hidden layer feedforward neural network with biogeography-based optimization for facial emotion recognition.RESULTS: The simulation results show that the overall accuracy of our method is 93.79±1.24%.CONCLUSION: This model is superior to the current mainstream facial emotion recognition models in the performance of facial emotion detection. In future research, we will try deep learning and other training methods.

Facial emotion recognition (FER) is the technology or process of identifying and interpreting human emotions based on the analysis of facial expressions. It involves using computer algorithms and machine learning techniques to detect and classify emotional states from images or videos of human faces. Further, FER plays a vital role in recognizing and understanding human emotions to better interpret someone’s feelings, intentions, and attitudes. In the present time, it is widely used in various fields such as healthcare, human–computer interaction, law enforcement, security, and beyond. FER has a wide range of practical applications across various industries including Emotion Monitoring, Adaptive Learning, and Virtual Assistants. This paper presents a comparative analysis of FER algorithms, focusing on deep learning approaches. The performance of different datasets, including FER2013, JAFFE, AffectNet, and Cohn–Kanade, is evaluated using convolutional neural networks (CNNs), deep face, attentional convolutional networks (ACNs), and deep belief networks (DBNs). Among the tested algorithms, DBNs outperformed other algorithms, reaching the highest accuracy of 98.82%. These results emphasize the effectiveness of deep learning techniques, particularly DBNs, in FER. Additionally, outlining the advantages and disadvantages of current research on facial emotion identification might direct future research efforts in the direction of the most profitable directions.

Previous studies that have assessed social cognition in Attention-Deficit/Hyperactivity Disorder (ADHD) have produced inconsistent findings. To summarize these data and shed light upon moderators that may explain observed inconsistencies, we conducted a systematic review and meta-analysis exploring social cognition (Theory of Mind (ToM), Empathy, Facial and Non-Facial Emotion Recognition) and Everyday Social Skills in children and adolescents with ADHD. The current meta-analysis involved 142 studies including 652 effect sizes. These studies compared children and adolescents with ADHD (n = 8,300) and with typical development (n = 7,983). Participants with ADHD exhibited moderate to very large deficits in ToM (SMD = 0.84, 95% CI = 0.68-0.99), Facial Emotion Recognition (SMD = 0.63, 95% CI = 0.46-0.81), and Everyday Social Skills (SMD = 1.23, 95% CI = 1.08-1.37). The magnitude of these impairments was similar when considering effect sizes adjusted for some covariates and the methodological quality of the studies. Few studies have investigated Empathy and Non-Facial Emotion Recognition, which precludes definitive conclusions. Children and adolescents with ADHD experience robust impairments in ToM, Facial Emotion Recognition and Everyday Social Skills. Future studies should explore whether these deficits are a consequence of difficulties in other areas of cognition (e.g., executive functioning). We have made all our raw data open access to facilitate the use of the present work by the community (e.g., clinicians looking for tools, assessing social impairments, or researchers designing new studies).

One of the ways humans communicate is by using facial expressions. Research on technology development in artificial intelligence uses deep learning methods in human and computer interactions as an effective system application process. One example, if someone does show and tries to recognize facial expressions when communicating. The prediction of the expression or emotion of some people who see it sometimes does not understand. In psychology, the detection of emotions or facial expressions requires analysis and assessment of decisions in predicting a person's emotions or group of people in communicating. This research proposes the design of a system that can predict and recognize the classification of facial emotions based on feature extraction using the Convolution Neural Network (CNN) algorithm in real-time with the OpenCV library, namely: TensorFlow and Keras. The research design implemented in the Raspberry Pi consists of three main processes, namely: face detection, facial feature extraction, and facial emotion classification. The prediction results of facial expressions in research with the Convolutional Neural Network (CNN) method using Facial Emotion Recognition (FER-2013) were 65.97% (sixty-five point ninety-seven percent).

BackgroundThe emotional state of individuals is difficult to identify and it is developing now a days because of vast interest in recognition. Many technologies have been developed to identify this emotional expression based on facial expressions, vocal expressions, physiological signals, and body expressions. Among these, facial emotion is very expressive for recognition using multimodalities. Understanding facial emotions has applications in mental well‐being, decision‐making, and even social change, as emotions play a crucial role in our lives. This recognition is complicated by the high dimensionality of data and non‐linear interactions across modalities. Moreover, the way emotion is expressed by people varies and these feature identification remains challenging, where these limitations are overcome by Deep learning models.MethodsThis research work aims at facial emotion recognition through the utilization of a deep learning model, named the proposed Residual Fused‐Graph Convolution Network (RF‐GCN). Here, multimodal data included is video as well as an Electroencephalogram (EEG) signal. Also, the Non‐Local Means (NLM) filter is used for pre‐processing input video frames. Here, the feature selection process is carried out using chi‐square, after feature extraction, which is done in both pre‐processed video frames and input EEG signals. Finally, facial emotion recognition and its types are determined by RF‐GCN, which is a combination of both the Deep Residual Network (DRN) and Graph Convolutional Network (GCN).ResultsFurther, RF‐GCN is evaluated for performance by metrics such as accuracy, recall, and precision, with superior values of 91.6%, 96.5%, and 94.7%.ConclusionsRF‐GCN captures the nuanced relationships between different emotional states and improves recognition accuracy. The model is trained and evaluated on the dataset and reflects real‐world conditions.

The ability to recognize emotion is important to wellbeing and building relationships with others, making this skill important in adolescence. Research investigating adolescents’ ability to recognize facial and vocal emotion expressions has reported differing conclusions about the pattern of emotion recognition across this developmental period. This systematic review aimed to clarify the pattern of recognition for facial and vocal emotion expressions, and the relationship of performance to different task and emotion expression characteristics. A comprehensive and systematic search of the literature was conducted using six databases. To be eligible for inclusion, studies had to report data for adolescents between 11 and 18 years of age and measure accuracy of the recognition of emotion cues in either the face or voice. A total of 2333 studies were identified and 47 met inclusion criteria. The majority of studies focused on facial emotion recognition. Overall, early, mid-, and late-adolescents showed a similar pattern of recognition for both facial and vocal emotion expressions with the exception of Sad facial expressions. Sex of the participant also had minimal impact on the overall recognition of different emotions. However, analysis showed considerable variability according to task and emotion expression characteristics. Future research needs to increase focus on recognition of complex emotions, and low-intensity emotion expressions as well as the influence of the inclusion of Neutral as a response option.

Sixty healthy women were included to the study. They were divided two group labeled women with PMS (n=33) and without PMS (n=27), which is accordance with the Premenstrual Assessment Form. Then, The Facial Emotion Recognition Test (56 mixed photos with happy, surprised, fearful, sad, angry, disgusted and neutral facial expressions from Ekman & Friesen's series) was performed on each group in both the luteal and follicular phases. The women with PMS were significantly worse in recognizing sad (p=0.003) and surprised (p=0.019) faces in the luteal phase compared to the follicular phase, whereas women without PMS were significantly worse in recognizing sad faces (p=0.008) in the luteal phase compared to the follicular phase. There were no significant differences between women with and without PMS in either the luteal phases or in the follicular phases according to facial emotion recognition (for each, p>0.05). Conclusıon: The women with PMS do not differ from women without PMS in recognizing facial emotions accurately. The low accuracy rate in the recognition of sad and surprised facial emotions in the luteal phase may lead PMS women to have more social problems.

The ability to perceive human facial emotions is an essential feature of various multi-modal applications, especially in the intelligent human-computer interaction (HCI) area. In recent decades, considerable efforts have been put into researching automatic facial emotion recognition (FER). However, most of the existing FER methods only focus on either basic emotions such as the seven/eight categories (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">happiness, anger</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">surprise</i> ) or abstract dimensions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">valence, arousal, etc.</i> ), while neglecting the fruitful nature of emotion statements. In real-world scenarios, there is definitely a larger vocabulary for describing human's inner feelings as well as their reflection on facial expressions. In this work, we propose to address the semantic richness issue in the FER problem, with an emphasis on the granularity of the emotion concepts. Particularly, we take inspiration from former psycho-linguistic research, which conducted a prototypicality rating study and chose 135 emotion names from hundreds of English emotion terms. Based on the 135 emotion categories, we investigate the corresponding facial expressions by collecting a large-scale 135-class FER image dataset and propose a consequent facial emotion recognition framework. To demonstrate the accessibility of prompting FER research to a fine-grained level, we conduct extensive evaluations on the dataset credibility and the accompanying baseline classification model. The qualitative and quantitative results prove that the problem is meaningful and our solution is effective. To the best of our knowledge, this is the first work aimed at exploiting such a large semantic space for emotion representation in the FER problem.

The authors examine facial emotion recognition and unirhinal olfactory performance in 19 schizophrenia patients and 14 comparison subjects. In patients, right nostril odor identification performance was positively related to overall emotion recognition accuracy, specifically, sad facial expressions. Olfactory and emotion recognition abilities appear significantly linked in schizophrenia.

Facial expressions have become a common way for interaction among humans. People cannot comprehend and predict the emotions or expressions of individuals through simple vision. Thus, in psychology, detecting facial expressions or emotion analysis demands an assessment and evaluation of decisions for identifying the emotions of a person or any group during communication. With the recent evolution of technology, AI (Artificial Intelligence) has gained significant usage, wherein DL (Deep Learning) based algorithms are employed for detecting facial expressions. The study proposes a system design that detects facial expressions by extracting relevant features using a Modified ResNet model. The proposed system stacks building-blocks with residual connections and employs an advanced extraction method with quantum computing, which significantly reduces computation time compared to conventional methods. The backbone stem utilizes a quantum convolutional layer comprised of several parameterized quantum-filters. Additionally, the research integrates residual connections in the ResNet-18 model with the Modified up Sampled Bottle Neck Process (MuS-BNP), retaining computational efficacy while benefiting from residual connections. The proposed model demonstrates superior performance by overcoming the issue of maximum similarity within varied facial expressions. The system's ability to accurately detect and differentiate between expressions is measured using performance metrics such as accuracy, F1-score, recall, and precision. This performance analysis confirms the efficacy of the proposed system, highlighting the advantages of quantum computing in feature extraction and the integration of residual connections. The model achieves quantum superiority, providing faster and more accurate computations compared to existing methodologies. The results suggest that the proposed approach offers a promising solution for facial expression recognition tasks, significantly improving both speed and accuracy.

Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.

Association between video gaming time and cognitive functions: A cross-sectional study of Chinese children and adolescents.

Facial expression recognition via ResNet-50

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods.