emotion classification

Non-verbal communication plays an important role in human communication. At the Delft University of Technology there is a project running on the automatic recognition of facial expressions. The developed system ISFER (Integrated System for Facial Expression Recognition) consists of modules suited for the analysis of a frontal view of the face. As the obtained results are still far from being perfect we present in this paper a new complementary approach based on a lateral view of the face. The underlying model is based on the silhouette of the facial profile with 10 characteristic points on specific places. The points are chosen so that using Matlab tool we can assess their locations as extreme points on the silhouette. From this information emotional facial expressions can be classified up to some degree using expert systems or neural networks. The new model and results of testing are reported in this paper.

Human Expression Recognition Clips Utilised Expert System [0] was designed to recognise facial expressions of the observed person in an automatic way. HERCULES forms a part of an Automated System for Non-verbal Communication [1] and represent the implementation of the facial expression analysis that the system performs. The theoretical formulation of facial expression recognition, whereupon inference engine of HERCULES is built, has been acquired from FACS [2]. HERCULES contains also a second inference ...

Artificial Intelligence (AI) has become a transformative force in social science research, enabling the analysis of large-scale, heterogeneous data to uncover latent patterns and predict complex human behaviors. Among AI's core methodologies, computer vision has evolved far beyond its early role in data acquisition to now encompass sophisticated systems capable of interpreting, analyzing, and synthesizing visual information across a range of socially relevant contexts. By integrating advanced image processing, machine learning, and computer graphics, computer vision empowers interdisciplinary investigations in psychology, sociology, and economics, modeling phenomena such as decision-making, emotional expression, and social interaction at unprecedented scales and resolutions. This paper presents a comprehensive survey of the state of the art in computer vision applications within the social sciences, with particular emphasis on recent breakthroughs in algorithms and enabling technologies that facilitate automated visual understanding. Key topics include object detection, facial recognition, scene understanding, and predictive modeling, which collectively underpin impactful applications in healthcare, autonomous systems, surveillance, and digital media. To structure this rapidly expanding domain, we propose a conceptual framework that organizes the field into four foundational pillars: image processing, object recognition, adaptive machine learning, and computer graphics. Each pillar contributes critical capabilities such as feature extraction, quality enhancement, semantic interpretation, and photorealistic rendering-functions that are increasingly pivotal in addressing contemporary social challenges. By critically evaluating current methodologies, benchmarking performance across domains, and identifying emergent trends, this work not only synthesizes existing knowledge but also outlines promising directions for future research at the intersection of AI and social science. Finally, we highlight how these advancements are reshaping societal norms and enabling AI-driven solutions to pressing global issues, from autonomous navigation to public health and beyond.

Evaluating the contact center agent's voice handling skills is important in order to enhance the customer satisfaction, through employee performance. Conventionally, supervisors in call center monitoring divisions listen to contact center agents' conversations and then assign scores for the voice of the agent which end up in most cases bias and as erroneous evaluations. In order to minimize the above mentioned problems, this paper discusses a system, we implemented to evaluate contact center agents' voices automatically avoiding human error and bias decisions. This software application considers speech rate, voice intensity level and emotional state of the contact center agent for their voice handling evaluations. According to the conducted research, it can be seen that the accuracy of evaluating the giving of instructions clearly using speech rate is 72%, evaluating listener comfortable voice intensity of agent is 83% and accuracy of emotion recognition of the agent&#39...

Facial emotion recognition is still a challenging task in computer vision because human facial expressions are very subtle and complex. In this paper, we address this issue and propose a novel deep-learning framework that combines multi-scale feature extraction with a dynamic attention mechanism and improved residual connection. The research aims to create a reliable system that identifies facial expressions correctly in different circumstances. The proposed method was validated rigorously on a standard face expression recognition data set, with an impressive overall accuracy of 96.1%. Additionally, the model performed remarkably well on extra metrics like precision, recall, and F1-score. These findings highlight the model's ability to learn and distinguish subtle features in human faces, leading to improved performance compared to conventional methods. In summary, this research makes a noteworthy contribution to affective computing by paving the way for the future development of real-time systems that can recognize human emotions, enabling numerous potential applications in the fields of mental health assessment, human-computer interaction, and adaptive user interfaces.

The distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and onedimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using Multimedia Tools and Applications

The distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and onedimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using Multimedia Tools and Applications

This article shows progress of brain signals interpretation which identifies some of the primary emotions; those primary emotions are learned by the cognitive Agent. As a result we expect collaborative work between two or more robots that have empathy with different emotions. As part of this article development, we present the following case study. Given that human beings feel and perceive all primary emotions, we are considering doing an experiment with 6 people forming 2 groups, group A will be made up of 3 women and group B will consist of 3 men, it is worth mentioning that the ages are between 20-24 years, and that such groups do not have mental problems. These groups were shown a set of images that express the primary emotions. For Group A: Anger, Surprise, Joy and Disgust for group B: Anger, Fear and Sadness.

Automatic emotion recognition plays a central role in the technologies underlying social robots, affect-sensitive human computer interaction design and affect-aware tutors. Although there has been a considerable amount of research on automatic emotion recognition in adults, emotion recognition in children has been understudied. This problem is more challenging as children tend to fidget and move around more than adults, leading to more self-occlusions and non-frontal head poses. Also, the lack of publicly available datasets for children with annotated emotion labels leads most researchers to focus on adults. In this paper, we introduce a newly collected multimodal emotion dataset of children between the ages of four and fourteen years old. The dataset contains 1102 audio-visual clips annotated for 17 different emotional states: six basic emotions, neutral, valence and nine complex emotions including curiosity, uncertainty and frustration. Our experiments compare unimodal and multimodal emotion recognition baseline models to enable future research on this topic. Finally, we present a detailed analysis of the most indicative behavioral cues for emotion recognition in children.

The important role of communication process between human and computer have been increased in the recent years. In this paper, the main focus on analyzing human brain signals to create a natural interaction between human brain and virtual human. The method of this study base on reading brain signals then classifies the signals in order to represent it as an avatar facial expression. These signals are associated with the inner emotion of the user. In order to get a real interaction between the internal emotion of a user and avatar facial expression, signal speed is used to clarify the difference of user situation for two emotions: happy and sad. The interactive process based on the relationship between human emotion and the velocity of brain signal from . As a result, this study shows a range of speed for each emotion which can be used to specify and represent the internal emotion of a user to create a natural interaction with virtual human. These results can be more realistic because it specifies the average of speed for each emotion.

Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories and these affective dimensions, and c) can perform basic appraisal-based emotion elicitation of situations based on a prompt-based computational implementation of the OCC appraisal model. These findings are highly relevant: First, they show that the ability to solve complex affect processing tasks emerges from language-based token prediction trained on extensive data sets. Second, they show the potential of large language models for simulating, processing and analyzing human emotions, which has important implications for various applications such as sentiment analysis, socially interactive agents, and social robotics.

Emotions play a fundamental role in the decision-making process, shaping human actions across diverse disciplines. The extensive usage of emotion intensity detection approaches has generated substantial research interest during the last few years. Efficient multi-label emotion intensity detection remains unsatisfactory even for high-resource languages, with a substantial performance gap among well-resourced and under-resourced languages. Team Tewodros participated in SemEval-2025 Task 11, Track B, focusing on detecting text-based emotion intensity. Our work involved multi-label emotion intensity detection across three languages: Amharic, English, and Spanish, using the (afro-xlmr-large-76L), (DeBERTa-v3-base), and (BERT-base-Spanish-wwm-uncased) models. The models achieved an average F1 score of 0.6503 for Amharic, 0.5943 for English, and an accuracy score of 0.6228 for Spanish. These results demonstrate the effectiveness of our models in capturing emotion intensity across multiple languages.

Natural Language Processing problems has recently been benefited for the advances in Deep Learning. Many of these problems can be addressed as a multi-label classification problem. Usually, the metrics used to evaluate classification models are different from the loss functions used in the learning process. In this paper, we present a strategy to incorporate evaluation metrics in the learning process in order to increase the performance of the classifier according to the measure we are interested to favor. Concretely, we propose soft versions of the Accuracy, micro-F 1 , and macro-F 1 measures that can be used as loss functions in the back-propagation algorithm. In order to experimentally validate our approach, we tested our system in an Emotion Classification task proposed at the International Workshop on Semantic Evaluation, SemEval-2018. Using a Convolutional Neural Network trained with the proposed loss functions we obtained significant improvements both for the English and the Spanish corpora.

The existing emotion recognition techniques based on the analysis of the tone of voice or facial expressions do not possess sufficient specificity and accuracy. These parameters can be significantly improved by employing physiological signals that escape the filters of human consciousness. The aim of this work was to carry out an EEG-based binary classification of emotional valence using a convolutional neural network and to compare its performance to that of a random forest algorithm. A healthy 30-year old male was recruited for the experiment. The experiment included 10 two-hour-long sessions of watching videos that the participant had selected according to his personal preferences. During the sessions, an electroencephalogram was recorded. Then, the signal was cleared of artifacts, segmented and fed to the model. Using a neural network, we were able to achieve a F1 score of 87%, which is significantly higher than the F1 score for a random forest model (67%). The results of our ex...

The study of facial emotion recognition (FER) holds signi cant importance within the realm of academic research, since it has wide-ranging rami cations across multiple areas such as mental health assessment and human-computer interaction. This work introduces a novel methodology for FER that integrates Gabor lter-based feature extraction with a deep Convolutional Neural Network (CNN). The utilisation of Gabor lters enables extraction of prominent textural characteristics from facial images, whilst CNNs acquire informative representations to achieve precise emotion classi cation. The proposed methodology is assessed using the FER2013 dataset and compared with pre-existing methodologies. The ndings illustrate the e cacy of our methodology in accurately identifying facial expressions of emotions, emphasising its potential for practical implementation in the elds of mental health research and emotion-sensitive systems. The method demonstrates improved accuracy and resilience by combining Gabor lters and CNNs, showing potential for enhancing mental health evaluation and enabling adaptive human-computer interaction. This study makes a valuable contribution to the elds of health, mental health, and adaptation by advancing the creation of emotion-aware technologies that are designed to address the unique emotional requirements of individuals.

This work introduces a novel method to consider subjectivity and general context dependency in text analysis. The proposed method takes into account subjectivity using a computational version of the Framework Theory by Marvin leveraging on text vectorization -such as Word2Vec by . The embeddings created by the vectorization represent the knowledge of the context to be used to for the text analysis. Our approach is based on three components: 1. a framework/"room" representing the point of view of the individual or the collective; 2. a benchmark/set of keywords representing the criteria for the analysis; and 3. the document to be analyzed. By measuring the similarity between the vectors representing words/semantic elements, we extract the relative relevance of the elements in the benchmark for the document to be analyzed. Our method provides a way to consider the point of view of the reader of the document or the specific domain we want to use to get insights. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text. Subjectivity is relevant to evaluate human reactions or to analyze text in a given context or domain.

Brain computer interface (BCIs) aims to communicate with external devices and also recognize human activities through brain signals. However, existing algorithms have some drawbacks, such as poor resolution, high-frequency noise, risk, and low accuracy. Various machine learning algorithms have been used to recognize human activities, but it is more complex to recognize the activities of humans. In order to solve these issues BCIs system based on modified DNN is proposed. Using a modified DNN algorithm, all human activities are recognized through the brain signals. The proposed method contains two phases such as training and testing the data. First, the Brain signal is taken as input, and then the next phase is pre-processing. The signal is pre-processed using a bandpass filter to remove noise present in the signal. Then the next phase is feature extraction, for that features are extracted from the pre-processed signal using Hybrid Adaptive Filtering (HAF) and Higher Order Crossing (...

The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala's psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic sign...

There are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings such as anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness by using several MATLAB functions namely, spectral descriptors, periodicity, and harmonicity. To accomplish the work, we analyze the CREMA-D (Crowd-sourced Emotional Multimodal Actors Data) & TESS (Toronto Emotional Speech Set) datasets of human speech. The audio file contains data that have various characteristics (e.g., noisy, speedy, slow) thereby the efficiency of the ML (Machine Learning) models increases significantly. The EMD (Empirical Mode Decomposition) is utilized for the process of signal decomposition. Then, the features are extracted through the use of several techniques such as the MFCC, GTCC, spectral centroid, rolloff point, entropy, spread, flux, harmonic ratio, energy, skewness, flatness, and audio delta. The data is trained using some renowned ML models namely, Support Vector Machine, Neural Network, Ensemble, and KNN. The algorithms show an accuracy of 67.7%, 63.3%, 61.6%, and 59.0% respectively for the test data and 77.7%, 76.1%, 99.1%, and 61.2% for the training data. We have conducted experiments using Matlab and the result shows that our model is very prominent and flexible than existing similar works.

Pain is rarely communicated alone, as it is often accompanied by emotions such as anger or sadness. Communicating these affective states involves shared representations. However, how an individual conceptually represents these combined states must first be tested. The objective of this study was to measure the interaction between pain and negative emotions on two types of facial representations of these states, namely visual (i.e., interactive virtual agents; VAs) and sensorimotor (i.e., one's production of facial configurations). Twenty-eight participants (15 women) read short written scenarios involving only pain or a combined experience of pain and a negative emotion (anger, disgust, fear, or sadness). They produced facial configurations representing these experiences on the faces of the VAs and on their face (own production or imitation of VAs). The results suggest that affective states related to a direct threat to the body (i.e., anger, disgust, and pain) share a similar facial representation, while those that present no immediate danger (i.e., fear and sadness) differ. Although visual and sensorimotor representations of these states provide congruent affective information, they are differently influenced by factors associated with the communication cycle. These findings contribute to our understanding of pain communication in different affective contexts.

It is unclear whether individuals with autism are impaired at recognizing basic facial expressions, and whether, if any impairment exists, it applies to expression processing in general, or to certain expressions, in particular. To evaluate these alternatives, we adopted a fine-grained analysis of facial expression processing in autism. Specifically, we used the 'facial expression megamix' paradigm [Young, A. W., Rowland, D., Calder, A.

Beliefs about conflict and uncertainty over felt emotions-for Joy, Pride, Sadness, Jealousy and Envy events-were studied by means of Yes/No and Why questions. Each participant (N = 1,156) judged a typical antecedent for a single emotion-e.g., Jealousy: story protagonist SP sees his or her partner kiss someone. The Yes/No results showed that SP was frequently expected to experience both phenomena, the more so the greater the event impact (Yes range: 40-86%). Beliefs associated with Yes answers (BY) were categorized into 4 categories: (BY1) reason-emotion opposition-felt emotions are unreasonable, inadequate ways of reacting; (BY2) ambivalent emotions-e.g., joy and sadness; (BY3) unclear emotions; (BY4) other causes-e.g., focused on event implications, SP's personality. No conflict or uncertainty answers (BN; range 14-60%) mirrored BY categories: (BN1) no reason-emotion opposition, (BN2) no ambivalent emotions, (BN3) clear emotions, (BN4) other causes. Attributions and beliefs about causes did not generally differ by gender. As a collective entity, expressed beliefs were complex, focusing on one or more emotion component-e.g., appraisal, regulation, expression-as well as on emotion intensity, duration, and on self-concept issues. Overall, expressed beliefs seemed to imply a malleability theory of emotions, and emotion awareness. Results overall confirmed the hypotheses that conflict and uncertainty attributions are more likely for: unpleasant experiences; when emotions are norm-incongruent for the judged event; when mixed, ambivalent emotions are felt. The study confirms that people interpret emotion processes according to their lay theories.

Although real-time facial emotion recognition is a hot topic research domain in the field of human-computer interaction, state-of-the-art available datasets still suffer from various problems such as some unrelated photos like document photos, unbalanced number of photos in each class, and some misleading images that can affect negatively on correct classification. 3RL dataset was created which contains about 24K images and will be publically available, to overcome previously available datasets problems. 3RL dataset is labelled with five basic emotions: happiness, fear, sadness, disgust, and anger. Moreover, we compared 3RL dataset with other most famous state-of-the-art datasets (FER dataset, CK+ dataset), we have applied the most common used algorithms in previous works, SVM and CNN. Results have shown a noticeable improvement of generalization on 3RL dataset. Experiments have shown an accuracy of up to 91.4% on 3RL dataset using CNN where results on FER2013, CK+ are respectively ...

In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to develop efficient and accurate models for emotion recognition and classification from audio data. This article presents an overview of advances in the field of emotion recognition and classification using AI with a particular focus on audio data. The article begins by discussing the importance of emotion recognition and its applications in various domains. The technical aspects of emotion recognition from audio data using AI are reviewed. It explores various machine learning and deep learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN) and convolutional neural networks (CNN) that have been successfully used in this context. In addition, the paper focuses on the training and evaluation of emotion recognition models. Potential applications and future directions of emotion recognition and classification based on audio data using AI are discussed. ...

We describe a new method for remote emotional state assessment using multispectral face videos, and present our findings: unique transdermal, cardiovascular and spatiotemporal facial patterns associated with different emotional states. The method does not rely on stereotypical facial expressions but utilizes different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) to gauge correlates of autonomic nervous system activity spatially and temporally distributed across the human face (e.g., blood flow, hemoglobin concentration, and temperature). We conducted an experiment where 110 participants viewed 150 short emotion-eliciting videos and reported their emotional experience, while three cameras recorded facial videos with multiple wavelengths. Spatiotemporal multispectral features from the multispectral videos were used as inputs to a machine learning model that was able to classify participants' emotional state (i.e., amusement, disgust, fear, sexual arousal, or no emotion) with satisfactory results (average ROC AUC score of 0.75), while providing feature importance analysis that allows the examination of facial occurrences per emotional state. We discuss findings concerning the different spatiotemporal patterns associated with different emotional states as well as the different advantages of the current method over existing approaches to emotion detection.

In this paper, we present a new direct access strategy for speaker identification system. DAMClass is a method for direct access strategy that speeds up the identification process without decreasing the identification rate drastically. This proposed method uses speaker classification strategy based on human voice's original characteristics, such as pitch, flatness, brightness, and roll off. DAMClass decomposes available dataset into smaller sub-datasets in the form of classes or buckets based on the similarity of speaker's original characteristics. DAMClass builds speaker dataset index based on rangebased indexing of direct access facility and uses nearest neighbor search, range-based searching and multiclass-SVM mapping as its access method. Experiments show that the direct access strategy with multiclass-SVM algorithm outperforms the indexing accuracy of range-based indexing and nearest neighbor for one to nine percent. DAMClass is shown to speed up the identification process 16 times faster than sequential access method with 91.05% indexing accuracy.

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions' features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets' data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website 'kaggle'. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques.

Facial emotions are the varying expressions of a person's face that communicate one's feelings and moods. Facial emotion in videos can be detected using techniques that analyze keyframes for facial muscle movements and patterns. However, these detections can be challenging due to potential simultaneous expressions and camera angle complexities. To overcome these pitfalls, this paper provides a practical framework for detecting facial emotions in videos. Firstly, the input key frames are preprocessed by MF and IN algorithms to acquire an enhanced image. Secondly, human detection and tracking occur using YOLOV7 and BYTE tracking algorithms. Then, the T-SNEVJ algorithm is used for face detection. Thirdly, facial landmark extraction using the HC technique, mesh generation, and feature extraction are done. Here, ED-SVR is utilized for mesh generation. In the meantime, feature point tracking followed by motion analysis is done using CC_OF. Finally, the GCRCNN algorithm classifies multi-facial emotions. The proposed system achieves a better accuracy and recall of 99.34% and 99.20%. Thus, the proposed methodology outperforms the existing FER techniques.

Most of the existing music recommendation systems use collaborative or content based recommendation engines. However, the music choice of a user is not only dependent to the historical preferences or music contents. But also dependent to the mood of that user. This paper proposes an emotion based music recommendation framework that learns the emotion of a user from the signals obtained via wearable physiological sensors. In particular, the emotion of a user is classified by a wearable computing device which is integrated with a galvanic skin response (GSR) and photo plethysmography (PPG) physiological sensors. This emotion information is feed to any collaborative or content based recommendation engine as a supplementary data. Thus, existing recommendation engine performances can be increased using these data. Therefore, in this paper emotion recognition problem is considered as arousal and valence prediction from multi-channel physiological signals. Experimental results are obtained on 32 subjects' GSR and PPG signal data with/out feature fusion using decision tree, random forest, support vector machine and k-nearest neighbors algorithms. The results of comprehensive experiments on real data confirm the accuracy of the proposed emotion classification system that can be integrated to any recommendation engine.

Commercial facial affect detection software is typically trained on large databases and achieves high accuracy in detecting basic emotions, but their use in educational settings is unclear. The goal of this research is to determine how basic emotions relate to the achievement emotion states that are more relevant in academic settings. Such relations, if accurate and consistent, may be leveraged to make more effective use of the commercial affect-detection software. For this study, we collected affect data over four days from a classroom study with 65 students using Betty's Brain. Basic emotions obtained from commercial software were aligned to achievement emotions obtained using sensor-free models. Interpretable classifiers enabled the study of relationships between the two types of emotions. Our findings show that certain basic emotions can help infer complex achievement emotions such as confusion, frustration and engaged concentration. This suggests the possibility of using commercial software as a less context-sensitive and more development-friendly alternative to the affect detector models currently used in learning environments.

The neurodevelopment of emotion recognition is critical to achieving an adequate Social Cognition. This ability is developed during the first years through primary social referents, and later peers are a source of training that facilitates insertion in social groups. Most of the emotion recognition tests used are based on reagents that use adult faces, which can be a problem when evaluating the ability to recognize basic emotions in children. The objective of the research was to study this ability in children, analyzing its incremental validity on the variables age, valence, gender and emotional category. The Recognition of Basic Emotions in Childhood Test (REBEC) was designed using 30 reagents based on faces of children expressing five basic emotions of different intensity (low, medium and high). The REBEC was administered to 214 children, between 6 and 12 years old. The maximum score was 30. The average (M) of correct answers was 19.86; with a standard deviation (SD) of ± 4.12. The study sample was divided into seven groups according to the level of schooling. The group of 12 years-old obtained the highest yield (M) 22.18 ± 4.12. The 6-years olds' group obtained the lowest yield (M) of 17.78 ± 4.41. The (M) of hits in girls was 20.61 ± 3.91; being of (M) 19.24 ± 4.21 for children. The Emotion Happiness was the most recognized (M) 93.8%, followed by Anger (M) 75.1%, Sadness (M) 58.6%, Disgust (M) 56.3%, and Fear (M) 47, 4%, We analyzed the level of success according to age, configuring the trajectory of Neurodevelopment for each emotion, taking incorrect categorization of emotions into account: disgust, tended to be confused with Anger or Sadness. After obtaining the normative data of the REBEC Test and observing its psychometric properties and the sensitivity for the detection of the differences between groups, it is postulated as a valid and reliable instrument for the measurement of the facial expression abilities of the basic emotions in childhood.

Synthesizing emotional speech by means of conversion from neutral speech allows us to generate emotional speech from many existing Text-to-Speech (TTS) systems. How much of the target emotion can be portrayed by the generated speech is largely dependent on the emotion data used to train the mapping function for voice transformation. In this paper, we introduce a method to pre-process the emotion database by detecting the emotions from speech using machine learning methods. A selection criterion is proposed to yield a refined database based on the results from emotion recognition. The experimental results have shown that the proposed data preprocessing method can effectively improve the naturalness of the synthesized speech by better portraying the targeted emotion. The quality of speech synthesized using the smaller database is comparable to that using the whole database. The computational load is reduced due to the reduction of the data used for training the transformation model.

Do people want to feel emotions that are familiar to them? In two studies, participants rated how much they typically felt various emotions (i.e., familiarity of the emotion) and how much they generally wanted to experience these emotions. We found that, in general, people wanted to feel pleasant emotions more than unpleasant emotions. However, for both pleasant and unpleasant emotions, people more (vs. less) familiar with an emotion also wanted to experience it more. Links between the familiarity of an emotion and wanting to experience that emotion were not explained by the concurrent experience of familiar emotions. Also, we show that although familiar emotions were also liked more, liking did not fully account for wanting familiar emotions. Finally, the familiarity of emotions mediated the links between trait affect and the emotions people wanted to feel. We propose that people are motivated to feel familiar emotions, in part, because of their instrumental value.

Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Stress &Emotion plays an important role in human and human communications in our daily life. Besides logical intelligence, emotional intelligence is considered an important part of human intelligence, which represents the ability to perceive, understand, and respond to emotion. However, the existing human-computer interaction systems still lack emotional intelligence. Affective brain-computer interactions aim to narrow the communication gap between humans and machines by developing computational models of emotion& stress. The feeling is an expression that exhaustively addresses human inclination, thought and conduct hence plays a significant job in relational human correspondence. Stress and Emotion assessment expects to consequently separate distinctive passionate states by utilizing physiological and non-physiological signs procured from humans to accomplish compelling correspondence and communication among humans and machines. Brainwaves-Based Stress and Emotion Estimation is per...

Neurons are the seat of movements of charged particles, which create microcurrents. Thanks to devices such as the EEG and the MEG, these brain electrical waves can be recorded on the surface of the skull. The frequency of these waves reflects the state of alertness and consciousness. The mathematical tools used to study emotions as an electromagnetic signal are diverse. Indeed, electromagnetic waves have been studied from different angles. The mathematical modeling of such a phenomenon leads back to the resolution of the system formed by the equation of Maxwell, Faraday, Gauss and Am-père. For this, we need the theory of the Fourier transformation. However, a study of an EEG graph, will lead a numeritian to be interested in the analysis of a signal and therefore in the theory of the discrete Fourier transformation and its algorithm the fast Fourier transformation. As the brain waves are represented by functions corresponding to small oscillations, it is possible to use the analsye of the wavelet transformation.

This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best-worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language.

First, we argue for the metaphysical claim that emotions are individuated as patterns of characteristic features. Our second claim concerns the epistemology of emotion recognition: We demonstrate that emotion recognition is a process pattern recognition that relies on the same type of pattern recognition typical for object recognition. The analogy allows us to defend a variant of a direct perception account of emotion recognition. We distinguish two forms of directly perceiving emotions: 1. perceiving an emotion (almost) without any topdown-processes, 2. perceiving an emotion involving some significant top-down-processes (including expectations and background knowledge), and in addition 3. an inferencebased evaluation of an emotion.

Crowd behavior is a critical aspect of numerous applications such as crowd management, urban planning, and safety monitoring in the current era of the world. Convolutional Neural Networks (CNNs), one of the most recent advancements in deep learning, have demonstrated potential in the analysis of crowd behavior patterns. However, computational limitations frequently make it difficult to implement complex CNN models for crowd analysis tasks, particularly in real-time applications. The utilization of a lightweight CNN model for crowd behavior analysis on the Motion Emotion Dataset (MED) is proposed in our study. The MED dataset has diverse scenes with varying crowd emotional and behavioral aspects, making it an ideal benchmark for evaluating crowd analysis algorithms. The 2D CNN model is applied to the MED datasets to extract the features and annotations for training the lightweight CNN. The model is validated in the validation set and achieved an accuracy of 99.4% on the Emotion Dataset and 94.35% on the Behavior Dataset. The results are validated using the confusion matrix. The results indicate that the lightweight CNN model achieves competitive performance on the MED dataset while exhibiting reduced computational overhead compared to more complex models. The discoveries made aid in the advancement of effective and scalable strategies for crowd surveillance and control, with applications spanning across diverse sectors such as public safety, transportation, and event coordination.

Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.

Human face expression Recognition is one of the most effective forms of social communication. Generally, facial expressions are a simple and obvious way for people to express their feelings and intentions. Typically, the goal of facial expression recognition is to categorize facial expressions into specific classes of expression labels. This paper presents a survey of facial emotion expression classification based on different machine learning and deep learning mechanisms and optimization algorithms. In order to evaluate the basic emotion of a person's face, a technology called facial expression recognition employs a computer as a helper with specific algorithms. Seven basic emotions were represented by facial expressions, including a smile, sadness, anger, disgust, surprise, fear, and a natural expression. In this paper, the focus is on using the Grey Wolf algorithm for selection of the optimal features from feature extraction from input image faces to recognize human facial em...

Background: This study developed a photo and video database of 4-to-6-year-olds expressing the seven induced and posed universal emotions and a neutral expression. Children participated in photo and video sessions designed to elicit the emotions, and the resulting images were further assessed by independent judges in two rounds.Methods: In the first round, two independent judges (1 and 2), experts in the Facial Action Coding System, firstly analysed 3,668 emotions facial expressions stimuli from 132 children. Both judges reached 100% agreement regarding 1,985 stimuli (124 children), which were then selected for a second round of analysis between judges 3 and 4.Results: The result was 1,985 stimuli (51% of the photographs) were produced from 124 participants (55% girls). A Kappa index of 0.70 and an accuracy of 73% between experts were observed. Lower accuracy was found for emotional expression by 4-year-olds than 6-year-olds. Happiness, disgust and contempt had the highest agreement...

Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative per...

Facial emotion recognition (FER) is a crucial task in human communication. Various face emotion recognition models were introduced but often struggle with generalization across different datasets and handling subtle variations in expressions. This study aims to develop the deep residual bidirectional long short-term memory (Bi-LSTM) fusion method to improve FER accuracy. This method combines the strengths of convolutional neural networks (CNN) for spatial feature extraction and Bi-LSTM for capturing temporal dynamics, using residual layers to address the vanishing gradient problem. Testing was performed on three face emotion datasets, and a comparison was made with seventeen models. The results show perfect accuracy on the extended Cohn Kanade (CK+) and the real-world affective faces database (RAF-DB) datasets and almost perfect accuracy on the face expression recognition plus (FERPlus) dataset. However, the receiver operating characteristic (ROC) curve for the CK+ dataset shows some inconsistencies, indicating potential overfitting. In contrast, the ROC curves for the RAF-DB and FERPlus datasets are consistent with the high accuracy achieved. The proposed method has proven highly efficient and reliable in classifying various facial expressions, making it a robust solution for FER applications.

Emotions represent a fundamental aspect when evaluating user satisfaction or collecting customer feedback in human interactions, as well as in the realm of human-computer interface (HCI) technologies. Moreover, as human beings, we possess a distinctive capacity for communication through spoken language. Recently, the realm of Speech Emotion Recognition (SER) has garnered substantial interest and gained significant traction within the domain of Natural Language Processing (NLP). Its primary objective remains the identification of various emotions, such as sadness, neutrality, and anger, from audio speech using a diverse array of classifiers. This paper conducts a comprehensive critical analysis of the existing Arabic SER studies. Furthermore, this research delves into the performance and constraints associated with these previous works. It also sheds light on the current promising trends aimed at enhancing methods for recognizing emotions in speech. To the best of our knowledge, this research stands as a pioneering contribution to the SER field, particularly in the context of reviewing existing Arabic studies.

Emotion recognition and emotional mimicry are both highly important for social interactions. The authors investigated in a subclinical sample if High Socially Anxious (HSA) individuals show an altered pattern of emotional mimicry, and exhibit difficulties in emotion recognition compared to Low Socially Anxious (LSA) individuals. Twenty-one HSA and 20 LSA participants were exposed to 60 dynamic facial expressions that gradually changed from neutral to full-intensity expressions of happiness, anger, sadness, disgust, or fear. Emotional mimicry was assessed using facial electromyography. Emotion recognition was measured after every picture and emotion regulation was measured by self-report. Compared to when participants saw neutral facial expressions, participants demonstrated significantly higher musculus (m.) corrugator supercilii activity of anger expressions, m. frontalis medialis activity of fear and sad expressions, m. levator labii activity of disgust, and m. zygomaticus major activity of happy expressions. HSA participants had a significantly higher m. levator labii activity of disgust expressions than LSA participants. Moreover, HSA participants showed a tendency toward impaired emotion recognition of negative facial expressions (p = 0.07). Results confirm emotion-specific emotional mimicry patterns for all five emotions. No differences for emotional mimicry between the two groups were found, except for subtle alterations in disgust in HSA individuals.

Emotion recognition and emotional mimicry are both highly important for social interactions. The authors investigated in a subclinical sample if High Socially Anxious (HSA) individuals show an altered pattern of emotional mimicry, and exhibit difficulties in emotion recognition compared to Low Socially Anxious (LSA) individuals. Twenty-one HSA and 20 LSA participants were exposed to 60 dynamic facial expressions that gradually changed from neutral to full-intensity expressions of happiness, anger, sadness, disgust, or fear. Emotional mimicry was assessed using facial electromyography. Emotion recognition was measured after every picture and emotion regulation was measured by self-report. Compared to when participants saw neutral facial expressions, participants demonstrated significantly higher musculus (m.) corrugator supercilii activity of anger expressions, m. frontalis medialis activity of fear and sad expressions, m. levator labii activity of disgust, and m. zygomaticus major activity of happy expressions. HSA participants had a significantly higher m. levator labii activity of disgust expressions than LSA participants. Moreover, HSA participants showed a tendency toward impaired emotion recognition of negative facial expressions (p = 0.07). Results confirm emotion-specific emotional mimicry patterns for all five emotions. No differences for emotional mimicry between the two groups were found, except for subtle alterations in disgust in HSA individuals.

Imbalanced training data always puzzles the supervised learning based emotion and sentiment classification. Several existing research showed that data sparseness and small disjuncts are the two major factors affecting the classification. Target to these two problems, this paper presents a word embedding based oversampling method. Firstly, a large-scale text corpus is used to train a continuous skip-gram model in order to form word embedding. A feature selection and linear combination algorithm is developed to construct text representation vector from word embedding. Based on this, the new minority class training samples are generated through calculating the mean vector of two text representation vectors in the same class until the training samples for each class are the same so that the classifiers can be trained on the fully balanced dataset. Evaluations on NLP&CC2013 Chinese micro blog emotion classification (multi-label) and English Multi-Domain Sentiment Dataset version 2.0 (single label) show that the proposed oversampling approach improves the imbalanced emotion/sentiment classification in Chinese (sentence level) and English (document level) obviously. Further analysis show that our approach can reduce the affection of data sparseness and small disjuncts in imbalanced emotion and sentiment classification.

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models.