Facial Expression Recognition - Science topic

The growing need for facial emotion recognition in various domains, particularly in online education, has driven advancements in Artificial Intelligence (AI) and computer vision. Facial expressions are a vital source of nonverbal communication as they convey a wide range of emotions through subtle changes in facial features. Recent developments in...

Facial Expression Recognition (FER) refers to the automated analysis of human emotional states using computer vision techniques, which are of significant importance in tasks such as fatigued driving detection, learning engagement analysis, and safety monitoring. In recent years, deep learning methods have made remarkable progress in facial expressi...

span id="docs-internal-guid-258bb08a-7fff-55a5-4aa0-24c60336d963"> Through the comparative examination of 30 subjects of clinical psychiatric relevance including 10 experts examined in prison, 10 psychiatric patients and 10 drug addicts with dual diagnosis in comparison with the expressions of 30 university students specifically involved in the res...

Personalized facial expression recognition (FER) involves adapting a machine learning model using samples from labeled sources and unlabeled target domains. Given the challenges of recognizing subtle expressions with considerable interpersonal variability, state-of-the-art unsupervised domain adaptation (UDA) methods focus on the multi-source UDA (...

Given the critical role that facial expressions play in human connection and communication, facial expression detection and recognition have attracted a lot of interest recently. The numerous uses of facial expression detection in a variety of industries, including virtual reality, intelligent tutoring systems, healthcare, and data-driven animation...

span lang="EN-US">Distance education has been prevalent since the late 1800s, but its rapid expansion began in the late 1990s with the advent of the online technological revolution. Distance learning encompasses all forms of training conducted without the physical presence of learners or teachers. While this mode of education offers great flexibili...

Facial expression plays a crucial role during interactions with people. Previous studies on facial expression recognition (FER) have mainly focused on adults, while there are few studies on FER for infants. Due to the apparent differences in facial proportions and facial contours between infants and adults, the FER studies for infants could not be...

Facial expression serves as a vital component of non-verbal communication, playing a significant role in human interactions and social dynamics. With advancements in computer vision and artificial intelligence, the automatic recognition of facial expressions has become an increasingly active research area with wide-ranging applications. This paper...

Facial expression identification has garnered considerable attention in recent years owing to its extensive applicability across various domains, including human-computer interaction, market research, and healthcare. The primary objective of Facial Emotion Recognition (FER) is to correlate various facial expressions with their corresponding emotion...

This study investigates the key characteristics and suitability of widely used Facial Expression Recognition (FER) datasets for training deep learning models. In the field of affective computing, FER is essential for interpreting human emotions, yet the performance of FER systems is highly contingent on the quality and diversity of the underlying d...

Vision-Language Models (VLMs) like CLIP offer promising solutions for Dynamic Facial Expression Recognition (DFER) but face challenges such as inefficient full fine-tuning, high complexity, and poor alignment between textual and visual representations. Additionally, existing methods struggle with ineffective temporal modeling. To address these issu...

We present our contribution to the 8th ABAW challenge at CVPR 2025, where we tackle valence-arousal estimation, emotion recognition, and facial action unit detection as three independent challenges. Our approach leverages the well-known Dual-Direction Attention Mixed Feature Network (DDAMFN) for all three tasks, achieving results that surpass the p...

Facial micro-expression recognition plays a crucial role in affective computing, human-computer interaction, and psychological analysis. This study implements a real-time face emotion detection system using the YOLOv8 (You Only Look Once version 8) model. The proposed approach leverages YOLO’s real-time processing capability and deep learning-based...

p>There is a rising demand for emerging machines that can be self-decisive and intelligent. Machines can capture the emotions and gestures of college students to mechanise tasks and handle interactions better. Facial expressions based on emotion recognition are practices that play a substantial role in the modern fields of artificial intelligence a...

Over 3 billion people live in rural, unincorporated areas globally, which are vital for habitation and production. The perceived safety of these landscapes significantly impacts health and well-being. However, rural areas, as natural environments for urban populations to connect with nature, have not been sufficiently addressed in terms of safety c...

Given the critical requirements for both speed and accuracy in facial expression recognition, this paper presents a novel deep-learning architecture named Fast Central Consistency Attention (FCCA). With FasterNet-s as its backbone network, FCCA is designed to recognize facial expressions. Firstly, we leverage partial convolution to extract features...

Facial expression recognition (FER) is significant in many application scenarios, such as driving scenarios with very different lighting conditions between day and night. Existing methods primarily focus on eliminating the negative effects of pose and identity information on FER, but overlook the challenges posed by lighting variations. So, this wo...

Dynamic Facial Expression Recognition (DFER) facilitates the understanding of psychological intentions through non-verbal communication. Existing methods struggle to manage irrelevant information, such as background noise and redundant semantics, which impacts both efficiency and effectiveness. In this work, we propose a novel supervised temporal s...

Dynamic facial expression recognition (DFER) is one of the most important challenges in computer vision, as it plays a crucial role in human–computer interaction. Recently, adapter-based approaches have been introduced into DFER, and they have achieved remarkable success. However, the adapters still suffer from the following problems: overlooking i...

Facial expression recognition (FER) serves as a vital interface for bridging human emotions and machine understanding, enabling applications across psychology , healthcare, and human-computer interaction. This study explores the performance of machine learning classifiers-SVM, Random Forest, KNN, and Decision Tree-on the CK+ dataset, a benchmark fo...

Facial Expression Recognition (FER) plays a foundational role in enabling AI systems to interpret emotional nuances, a critical aspect of affective Theory of Mind (ToM). However, existing models often struggle with poor calibration and a limited capacity to capture emotional intensity and complexity. To address this, we propose Ranking the Emotiona...

Facial expression recognition (FER) plays a crucial role in domains such as healthcare and access security. Traditional models primarily utilize convolutional networks to extract features like facial landmarks and positions of facial features. However, these methods often result in feature maps with significant redundancy, contributing minimally to...

Student success is a crucial metric for educational institutions, as it affects not only the individual student's academic and personal growth but also the institution's reputation, funding, and overall educational outcomes. Student failure is a common and concerning issue in educational institutions, often leading to significant academic and perso...

Este trabajo presenta el desarrollo de un sistema de reconocimiento facial y análisis emocional en tiempo real que se implementa en Python y utiliza una variedad de bibliotecas especializadas. Para capturar imágenes faciales en vivo, el sistema utiliza una cámara web y una aplicación móvil creada con.NET MAUI. Los datos se envían a un servidor Flas...

With the rapid development of neural networks, emotion recognition has become a research area of great concern. It has important applications not only in marketing and human-computer interaction but also holds significant importance for improving emotional computing and user experience. This paper studies various methods for emotion recognition in...

Facial expressions, as a vital conduit for human emotional expression, are among the most observable features of machines in the field of computer vision. Consequently, facial expression recognition holds broad potential for applications in artificial intelligence and health monitoring, among others. Given the diversity and complexity of expression...

Building AI systems, including Facial Expression Recognition (FER), involves two critical aspects: data and model design. Both components significantly influence bias and fairness in FER tasks. Issues related to bias and fairness in FER datasets and models remain underexplored. This study investigates bias sources in FER datasets and models. Four c...

In the contemporary era of intelligent connectivity, Affective Computing (AC), which enables systems to recognize, interpret, and respond to human behavior states, has become an integrated part of many AI systems. As one of the most critical components of responsible AI and trustworthiness in all human-centered systems, explainability has been a ma...

The rapid aging of the global population has highlighted the need for technologies to support elderly, particularly in healthcare and emotional well-being. Facial expression recognition (FER) systems offer a non-invasive means of monitoring emotional states, with applications in assisted living, mental health support, and personalized care. This st...

With the rise of artificial intelligence, machine learning (ML) is increasingly integrated into daily life. Facial expressions, lasting about 1/20th of a second and difficult to conceal, convey nuanced emotions beyond words. They are categorized as macro expressions, displayed under normal circumstances, and micro expressions, fleeting and subconsc...

Deep learning techniques are becoming increasingly important in the field of facial expression recognition, especially for automatically extracting complex features and capturing spatial layers in images. However, previous studies have encountered challenges such as complex data sets, limited model generalization, and lack of comprehensive comparat...

Facial expression recognition (FER) has emerged as a promising approach to the development of emotion-aware intelligent agents and systems. However, key challenges remain in utilizing FER in real-world contexts, including ensuring user understanding and establishing a suitable level of user trust. We developed a novel explanation method utilizing F...

Objective The aim of this study is to evaluate the ability to detect emotions from human facial expressions via facial recognition technologies and analyze the effectiveness of deep learning models in this process. Method This research was conducted between 01.04 and 01.07.2024. The data of the study were taken from the open access site https://ww...

More virtual reality (VR) scenarios have become more prevalent in recent years. More and more people are getting into VR, meaning that objective physiological measures to assess a user's emotional state automatically are becoming more critical. Individuals’ emotional states impact their behaviour, opinions, emotions, and decisions. They may be used...

Facial expression recognition (FER) is an essential technology at the intersection of artificial intelligence (AI), computer vision, and psychology. This study proposes a novel framework for FER, aiming to improve system robustness and generalization, especially under variable real-world conditions. Using the FER2013 dataset, this research combines...

Facial expressions serve as a means of conveying human emotions and individual intentions. The ability to perceive and interpret facial emotions is a relatively effortless job for humans, although it poses significant challenges when attempting to replicate this capability using a computer. Facial expressions can be detected from static photos, vid...

The most generic and understandable way of communication is by observing facial expressions; Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural facial expressions. The Viola-Jones algorithm det...

This study presents an improved Facial Expression Recognition (FER) model using Swin transformers for enhanced performance in detecting mental health through facial emotion analysis. In addition, some techniques involving better dropout and layer-wise unfreezing were implemented to reduce model overfitting. This study evaluates the proposed models...

The cognitive emotions of individuals during tasks largely determine the success or failure of tasks in various fields such as the military, medical, industrial fields, etc. Facial video data can carry more emotional information than static images because emotional expression is a temporal process. Video-based Facial Expression Recognition (FER) ha...

Recent advancements in dynamic facial expression recognition (DFER) have predominantly utilized static features, which are theoretically inferior to dynamic features. However, models fully trained with dynamic features often suffer from over-fitting due to the limited size and diversity of the training data for fully supervised learning (SL) models...

The EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. In particular, the system's human sensing capabilities allow for the perception of emotional states to provide a personalized experience. This paper outlines the development of the emo...

Facial Expression Recognition (FER) and Human Activity Recognition (HAR) are key areas in wearable computing, with applications in healthcare, fitness, and human-computer interaction. This study explores the use of Emteq’s OCOsense<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> smart glasses f...

Infant facial expression recognition is of great importance for early care, which can help parents and caregivers to identify the emotional state of infants. In this study, we propose an improved YOLOv8 model for infant facial expression recognition. Compared with traditional YOLOv8 model, following improvements have been proposed. First, the Swin...

Adult facial expression recognition (FER) is essential for human-computer interaction, mental health assessment, and social robotics applications because it improves user experiences and emotional well-being. This study presents a novel attention mechanism-based transformer approach designed to capture detailed patterns in facial features and dynam...

Evaluating affect analysis methods presents challenges due to inconsistencies in database partitioning and evaluation protocols, leading to unfair and biased results. Previous studies claim continuous performance improvements, but our findings challenge such assertions. Using these insights, we propose a unified protocol for database partitioning t...

The recognition of babies’ facial expressions is challenging due to the limited availability of annotated data and the complex nature of their emotions.To address this problem, this work introduces a novel dataset, FER-BYC (Facial Expression Recognition for Bangladeshi Young Children), comprising 1,425 annotated images of babies’ facial expressions...

Görme engelli bireylerin sosyal yaşamda karşılaştıkları temel zorluklardan biri, etkileşim sırasında karşılarındaki kişilerin kimliğini ve duygusal durumlarını algılayamamaktır. Bu durum, iletişimde belirsizliklere ve yanlış anlamalara neden olarak bireylerin sosyal uyumlarını ve yaşam kalitelerini olumsuz etkileyebilir. Yapay zeka ve görüntü işlem...

Facial Expression Recognition (FER) is currently a very active field of research. It involves a computer’s capability to recognize and interpret human emotional expressions, which change with an individual’s internal emotional state. Several researchers have been working on this topic, using classical methods or Neural Network (NN) approaches. The...

Facial expression recognition (FER) is significant for daily mental health monitoring because facial expressions reflect an individual's mental condition. However, it is still hard to achieve accurate and convenient FER using wearable devices. Here, a high‐accuracy, self‐powered, and intelligent FER system is reported consisting of a triboelectric...

Facial expressions are the most basic non-verbal method people use to communicate feelings, intentions and reactions without words. Recognizing these facial expressions accurately is essential for a variety of applications — such as tools that use our faces to interact with computers (human-computer interaction, or HCI), security systems and emotio...

Many times automatic human emotional detection plays a vital role in health care and law enforcement agencies. To achieve this, it is necessary to have a robust hardware resource that can capture images under harsh conditions; numerous components already exist in the industry for this purpose. However, the greatest challenge lies in developing an a...

This paper expands the cascaded network branch of the autoencoder-based multi-task learning (MTL) framework for dynamic facial expression recognition, namely Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition (MTCAE-DFER). MTCAE-DFER builds a plug-and-play cascaded decoder module, which is based on the Vision Transformer (ViT...

Facial expression recognition is a fundamental task in computer vision with a wide range of applications, from human-computer interaction to emotional analysis. This research project delves into the development and implementation of a Facial Expression Recognition System (FERS) using Convolutional Neural Networks (CNNs). The primary objective of th...

The exploration of sentiments through facial expressions is a captivating domain with applications across security, healthcare, and human–computer interaction, where understanding sentiments is primarily about interpreting an individual's stance from a piece of text. However, in the context of non-verbal communication, it extends to the interpretat...

In this study, we explored the potential of utilizing Facial Expression Activations (FEAs) captured via the Meta Quest Pro Virtual Reality (VR) headset for Facial Expression Recognition (FER) in VR settings. Leveraging the EmojiHeroVR Database (EmoHeVRDB), we compared several unimodal approaches and achieved up to 73.02% accuracy for the static FER...

Visual biosignals can be used to analyze human behavioral activities and serve as a primary resource for Facial Expression Recognition (FER). FER computational systems face significant challenges, arising from both spatial and temporal effects. Spatial challenges include deformations or occlusions of facial geometry, while temporal challenges invol...

After a stroke, most patients suffer from various physical movement disorders. Recovery of paralyzed parts can be facilitated through proper rehabilitation exercises. However, many paralyzed patients rely on others for assistance with daily activities and rehabilitation therapy. Consequently, they often do not perform rehabilitation exercises regul...

After a stroke, most patients suffer from various physical movement disorders. Recovery of paralyzed parts can be facilitated through proper rehabilitation exercises. However, many paralyzed patients rely on others for assistance with daily activities and rehabilitation therapy. Consequently, they often do not perform rehabilitation exercises regul...

Single‐mode sensors suffer from poor robustness and insufficient data features in facial expression recognition, so fusing multi‐sensor signals is the key to improving the accuracy of expression recognition systems. Here, a biocompatible capacitive‐electromyographic dual‐mode sensor (CEDS) is presented, consisting of a capacitive pressure sensing u...

Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expressions has been extensively studied by the Computer Vision research society. But Facial Expression Recognition in real-world is still a challenging task, p...

Deep learning has grabed more and more interest in recent year’s in order to the development and implementation of big data. Convolutional neural networks that are deep learning neural networks are crucial for facial picture identification. In this paper, a model recognizes facial expressions and suggests music and recommends book based on related...

Facial Expression Recognition (FER) has been widely explored in realistic settings; however, its application to artistic portraiture presents unique challenges due to the stylistic interpretations of artists and the complex interplay of emotions conveyed by both the artist and the subject. This study addresses these challenges through three key con...

Facial expression recognition (FER) systems raise significant privacy concerns due to the potential exposure of sensitive identity information. This paper presents a study on removing identity information while preserving FER capabilities. Drawing on the observation that low-frequency components predominantly contain identity information and high-f...

This study enhances the generalization ability and recognition accuracy of convolutional neural networks (CNNs) in educational settings, particularly in the task of Facial Expression Recognition (FER), to support effective analysis of classroom teaching behaviors. A novel multimodal teaching behavior analysis model is proposed to achieve this goal,...

As a key area in artificial intelligence and computer vision, facial expression analysis technology has been taking hold rapidly, yielding dazzling advances. This technology extracts even the subtlest facial movements to identify when a human is sad, happy, or angry. It has extensive application value in markets like mental diagnosis, secure monito...

Sheep facial expressions are valuable indicators of their pain levels, playing a critical role in monitoring their health and welfare. In response to challenges such as missed detections, false positives, and low recognition accuracy in sheep facial expression recognition, this paper introduces an enhanced algorithm based on YOLOv8n, referred to as...

Facial Expression Recognition has a wide application prospect in social robotics, health care, driver fatigue monitoring, and many other practical scenarios. Automatic recognition of facial expressions has been extensively studied by the Computer Vision research society. But Facial Expression Recognition in real-world is still a challenging task, p...

Traditional facial expression recognition (FER) approaches for understanding human emotional signals have limitations such as preprocessing, feature extraction, and multi-stage classification, which require significant processing power and computational complexity. Nevertheless, an advanced object detection model like YOLOv8 model is favoured for i...

Annotation ambiguity caused by the inherent subjectivity of visual judgment has always been a major challenge for Facial Expression Recognition (FER) tasks, particularly for largescale datasets from in-the-wild scenarios. A potential solution is the evaluation of relatively objective emotional distributions to help mitigate the ambiguity of subject...

This comprehensive review delves deeply into the various methodologies applied to facial expression recognition (FER) through the lens of graph representation learning (GRL). Initially, we introduce the task of FER and the concepts of graph representation and GRL. Afterward, we discuss some of the most prevalent and valuable databases for this task...

Facial Expression Recognition (FER) has become one of the most widely utilized techniques for assessing the emotional state of vehicle operators, aimed at preventing traffic accidents. In recent years, deep Convolutional Neural Networks (CNNs) have been extensively employed for FER tasks, achieving significant advancements and demonstrating their e...

Facial expressions exhibit inherent similarities, variability, and complexity. In real-world scenarios, challenges such as partial occlusions, illumination changes, and individual differences further complicate the task of facial expression recognition (FER). To further improve the accuracy of FER, a Multi-head Attention Affinity and Diversity Shar...

The purpose of this study was to determine whether previously established visual attention patterns remained intact during video scenes designed to elicit specific emotions using a novel suite of biosensors. To examine the relationship between visual attention and emotion, data from eye tracking, facial expression recognition (FER), and galvanic sk...

Facial expression recognition (FER) is a critical technology with applications spanning healthcare, security, and human-computer interaction. This study explores the development of a sophisticated FER system leveraging deep learning techniques to overcome existing challenges in accuracy, robustness, and practical deployment. The research methodolog...

The intelligent processing of human facial expressions has gained popularity due to the growth of big data and the expanding knowledge of AI and machine learning. In this paper, the author reviews the current mainstream methods for face recognition using convolutional neural network (CNN) models in deep learning, providing insights and future direc...

Face images are the primary non-verbal, face-to-face communication method used in criminal detection, depression analysis, mental disease identication, health forecasting, and other applications. As a result, there has been a lot of study on emotion recognition using face images in recent years. However, it remains a big challenge, especially when...

Emotion detection is a critical aspect of human-computer interaction and various other applications, including real-time video surveillance, psychological studies and social media emotion analysis. Understanding the complexity and nuance of human emotions requires an in-depth look at emotion intensity. Furthermore, occlusion is a common problem in...

Facial expression recognition is a current trend in computer vision to identify human emotional states from facial cues. There are six basic human facial expressions: anger, disgust, fear, happiness, surprise, and sadness. Popular architectures like ResNet need development to improve accuracy and reduce parameter utilization. This study proposes a...

Automated Facial Expression Recognition (FER) is challenging due to intra-class variations and inter-class similarities. FER can be especially difficult when facial expressions reflect a mixture of various emotions (aka compound expressions). Existing FER datasets, such as AffectNet, provide discrete emotion labels (hard-labels), where a single cat...

Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-...

In smart classroom environments, accurately recognizing students’ facial expressions is crucial for teachers to efficiently assess students’ learning states, timely adjust teaching strategies, and enhance teaching quality and effectiveness. In this paper, we propose a student facial expression recognition model based on multi-scale and deep fine-gr...

The effectiveness of facial expression recognition (FER) algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data. However, labeling large datasets demands significant human, time, and financial resources. Although active learning methods have mitigated the dependency on extensive labeled data...