AffectNet

doi:doi:10.57702/8qx7ouup

AffectNet

The dataset consists of a significant collection of 60,000 facial expression images, categorized into eight different classes, including neutral, happy, angry, sad, fear, surprise, disgust, and contempt. The dataset also includes the intensity of valence and arousal associated with each expression.

BibTex:

@dataset{Ali_Mollahosseini_and_Behzad_Hasani_and_Mohammad_H_Ma-hoor_2024,
    abstract = {The dataset consists of a significant collection of 60,000 facial expression images, categorized into eight different classes, including neutral, happy, angry, sad, fear, surprise, disgust, and contempt. The dataset also includes the intensity of valence and arousal associated with each expression.},
    author = {Ali Mollahosseini and Behzad Hasani and Mohammad H Ma-hoor},
    doi = {10.57702/8qx7ouup},
    institution = {No Organization},
    keyword = {'AffectNet', 'Affective Computing', 'Arousal', 'Deep Convolutional Neural Networks', 'Emotion Recognition', 'Facial Emotion Recognition', 'Facial Expression', 'Facial Expression Analysis', 'Facial Expression Recognition', 'Facial Expressions', 'Facial Occlusion', 'Human Emotions', 'Image', 'Image Dataset', 'Real-world FER', 'Valence', 'arousal', 'emotion recognition', 'facial expression', 'facial landmarks', 'valence'},
    month = {dec},
    publisher = {TIB},
    title = {AffectNet},
    url = {https://service.tib.eu/ldmservice/dataset/affectnet},
    year = {2024}
}