Improved optimizer with deep learning model for emotion detection and classification.

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.

Background/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust, Happy, Surprise, Neutral, Fear), so we created a Low Resolution Facial Expression (LRFE) dataset, which contains more than 6000 images of seven types of facial expressions. The existing FER2013 dataset and LRFE dataset were used. These datasets were divided in the ratio 80:20 for training and testing and validation purpose. A HDM is proposed, which is a combination of Gaussian Filter, Bilateral Filter and Non local means denoising Filter. This hybrid denoising method helps us to increase the performance of the convolutional neural network. The proposed model was then compared with VGG16 and VGG19 models. Findings: The experimental results show that the proposed FERConvNet_HDM approach is effective than VGG16 and VGG19 in facial expression recognition on both FER2013 and LRFE dataset. The proposed FERConvNet_HDM approach achieved 85% accuracy on Fer2013 dataset, outperforming the VGG16 and VGG19 models, whose accuracies are 60% and 53% on Fer2013 dataset respectively. The same FERConvNet_HDM approach when applied on LRFE dataset achieved 95% accuracy. After analyzing the results, our FERConvNet_HDM approach performs better than VGG16 and VGG19 on both Fer2013 and LRFE dataset. Novelty/Applications: HDM with convolutional neural networks, helps in increasing the performance of convolutional neural networks in Facial expression recognition. Keywords: Facial expression recognition; facial emotion; convolutional neural network; deep learning; computer vision

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).

Automated facial emotion recognition (FER) is one of the important fields of human-computer interaction (HCI). FER remains challenging due to facial accessories, non-uniform illumination, pose variation, etc. Emotion detection exploiting conventional algorithms has the demerit of mutual optimization of classification and feature extraction. Artificial intelligence (AI) techniques can be employed to identify FER automatically. Deep learning (DL) driven FER models have recently allowed for designing an end-to-end learning process. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning Based FER (HGSO-DLFER) technique for HCI. The HGSO-DLFER technique aims to recognize and identify various kinds of facial emotions. To accomplish this, the HGSO-DLFER technique employs adaptive fuzzy filtering (AFF) for noise removal. In addition, the MobileNet model is used for feature vector generation, and the HGSO algorithm optimally chooses its hyperparameter scan. For the recognition of facial emotions, the HGSO-DLFER technique uses an autoencoder (AE) classifier with a Nadam optimizer. A widespread experimental analysis is made to facilitate a better understanding of the FER results by the HGSO-DLFER technique. The comparative analysis showed the effective performance of the HGSO-DLFER technique over other FER techniques with maximum accuracy of 98.65%.

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.

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).

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.

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.

Emotions play a key role in communication between humans. However, it is a characteristic missing in standard human-machine interaction. Therefore, the development of automatic systems for recognizing human emotions has a high value nowadays. In this article is studied the impact of Transfer Learning (TL) methodology over a Deep Learning (DL) system created for Facial Emotion Recognition (FER). A Convolutional Neural Network with a VGG-type architecture is developed with the use of a large image-based FER data set (known as FER2013). TL methodology using different approaches is performed in order of reusing the knowledge assimilated over FER2013 data set, and it is evaluated over another FER data set (known as CK+). We show how a TL procedure can increase the accuracy and generalization ability of a DL model for recognizing basic emotions.KeywordsEmotion recognitionNeural networksDeep learningTransfer learningConvolutional neural networks

Facial emotion recognition (FER) is vital for human-computer interactions such as clinical practice and behavioral description. Accurate and robust FER using computer models remains a difficulty, because of the non-uniformity of human features and visual differences such as diverse facial positions and illumination. Among all FER model strategies, Convolutional Neural Networks (CNNs) show considerable promise owing to their excellent automated feature extraction and computational efficiency. In this study, we obtained the greatest single-network classification accuracy on the FER2013 dataset. We take the VGG Net architecture, carefully tweak its hyperparameters, and experiment with several optimization strategies. According to our experiments, our model has accuracy of 88.62 percent on FER2013 without needing extra training data. In this study, we apply Resnet and Dense Net models and use the FER2013 dataset to show algorithmic differences and performance implications. The Resnet model produced an accuracy of 88.62 percent, while the Dense Net model produced an accuracy of 85.83 percent.

In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective.

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.

Traditional interviewing literature has regularly highlighted its vulnerability to human subjectivity and unconscious bias, as such pitfalls may cause interviewers to overlook critical behavioral cues. To address these shortcomings, the present study proposes an artificial intelligence-based assessment tool, the Interview Video Analysis System (IVAS), which integrates Facial Emotion Recognition (FER), Gaze Tracking, and Audio Analysis technologies into a single, cohesive system to evaluate candidates objectively. IVAS comprises a Convolutional Neural Network (CNN) based on 22 layers trained on the FER-2013 dataset, achieving an accuracy of 86% in recognizing seven different emotions: Anger, Fear, Sadness, Happiness, Disgust, Surprise, and Neutral, through sophisticated data augmentation and hyperparameter optimization. The system utilizes 68-point facial features from dlib, with its gaze module measuring eye contact, directional changes, and blinks, relating these metrics to engagement and confidence. Additionally, the audio component employs Llama 3.2 with 11 billion parameters and Mel-Frequency Cepstral Coefficients (MFCC) to extract voice features such as pitch, hesitancy, and fluency, and transcribes speech for linguistic analysis. The late-fusion logic aggregates outputs from each module into a well-organized system that provides percentage measures and performance levels. As a Streamlit web application, IVAS can process live and recorded interviews in real-time, offering recruiters a scalable, data-driven assessment tool. This system outperforms other state-of-the-art models by approximately 12 to 30% in FER efficiency and is the first to incorporate multimodal behavioral analysis to reduce ambiguities inherent in unimodal methods. Collectively, this comprehensive system enhances traditional interviewing techniques by providing standardized, bias-proof insights into candidate compatibility.