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Refresh. Images of people showing eight different emotions, face dataset.
Facial Emotion Recognition Dataset. The dataset consists of images capturing people displaying 7 distinct emotions ( anger, contempt, disgust, fear, happiness, sadness and surprise ).
Face emotion recognition technology detects emotions and mood patterns invoked in human faces. This technology is used as a sentiment analysis tool to identify the six universal expressions, namely, happiness, sadness, anger, surprise, fear and disgust. Identifying facial expressions has a wide range of applications in human social interaction d…
Aug 22, 2023 · Face Emotion Recognition Dataset (FER+) The FER+ dataset is a notable extension of the original Facial Expression Recognition (FER) dataset. Developed to improve upon the limitations of the original dataset, FER+ offers a more refined and nuanced labeling of facial expressions.
Apr 7, 2022 · The Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions. We collected data from 43 participants who watched...
May 8, 2021 · Abstract: Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting.
Facial Expression Recognition (FER) 130 papers with code • 24 benchmarks • 29 datasets. Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face.
