Related papers: Leaving Some Facial Features Behind
The COVID-19 pandemic has undoubtedly changed the standards and affected all aspects of our lives, especially social communication. It has forced people to extensively wear medical face masks, in order to prevent transmission. This face…
Emotions play a central role in the social life of every human being, and their study, which represents a multidisciplinary subject, embraces a great variety of research fields. Especially concerning the latter, the analysis of facial…
In this paper, we present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features from facial images and incorporates contextual information,…
Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an…
This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitive to a large range of…
Detection of human emotions based on facial images in real-world scenarios is a difficult task due to low image quality, variations in lighting, pose changes, background distractions, small inter-class variations, noisy crowd-sourced…
Efficient data compression is crucial for the storage and transmission of visual data. However, in facial expression recognition (FER) tasks, lossy compression often leads to feature degradation and reduced accuracy. To address these…
Identifying human emotions using AI-based computer vision systems, when individuals wear face masks, presents a new challenge in the current Covid-19 pandemic. In this study, we propose a facial emotion recognition system capable of…
Facial expression recognition is a topic of great interest in most fields from artificial intelligence and gaming to marketing and healthcare. The goal of this paper is to classify images of human faces into one of seven basic emotions. A…
Facial expression recognition (FER) algorithms classify facial expressions into emotions such as happy, sad, or angry. An evaluative challenge facing FER algorithms is the fall in performance when detecting spontaneous expressions compared…
Facial features deformed according to the intended facial expression. Specific facial features are associated with specific facial expression, i.e. happy means the deformation of mouth. This paper presents the study of facial feature…
Throughout the various ages, facial expressions have become one of the universal ways of non-verbal communication. The ability to recognize facial expressions would pave the path for many novel applications. Despite the success of…
This paper considers the problem of error correction in multi-class classification of face images on unbalanced samples. The study is based on the analysis of a data frame containing images labeled by seven different emotional states of…
In this paper we propose an easiest approach for facial expression recognition. Here we are using concept of SVM for Expression Classification. Main problem is sub divided in three main modules. First one is Face detection in which we are…
Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak…
Expression recognition holds great promise for applications such as content recommendation and mental healthcare by accurately detecting users' emotional states. Traditional methods often rely on cameras or wearable sensors, which raise…
To fully understand the complexities of human emotion, the integration of multiple physical features from different modalities can be advantageous. Considering this, we present an analysis of 3D facial data, action units, and physiological…
Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments.…
Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and…
Facial expression recognition has been an active research area over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT,…