44 datasets found

Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses,...

The FERPlus dataset is a large-scale facial expression recognition dataset containing 28,709 training images, 3,589 validation images, and 3,589 test images.

Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions.

FER2013 is a facial expression recognition dataset.

AffectNet is a large-scale facial expression recognition dataset that contains over one million facial images collected from the Internet.

Face2exp: Combating data biases for facial expression recognition.

Representation learning and feature disentanglement have recently attracted much research interests in facial expression recognition. The ubiquitous ambiguity of emotion labels...

Aff-Wild2 is a large-scale in-the-wild database for affective behavior analysis, containing 564 videos with frame-level annotations for valence-arousal estimation, facial action...

The BU-3DFE database contains 3D face scans and images of 100 persons (56 female and 44 male), with varying ages (18-70 years) and diverse ethnic/racial ancestries. Each subject...

The AffectNet dataset is a facial expression dataset, consisting of images of faces with emotional expressions.

The SD-MATH dataset is a public dataset for engagement detection from videos. It contains 20 videos and 20 subjects.

The HBCU dataset is a public dataset for engagement detection from videos. It contains 120 videos and 34 subjects.

The DAiSEE dataset is a public dataset for engagement detection from videos. It contains 9068 videos and 112 subjects.

The WACV dataset is a public dataset for engagement detection from videos. It contains 4424 3-channel images of varying sizes.

The SFEW dataset is a subset of EmotiW2015 in-the-wild emotion dataset.

Label distribution learning on auxiliary label space graphs for facial expression recognition.

The dataset consists of a significant collection of 60,000 facial expression images, categorized into eight different classes, including neutral, happy, angry, sad, fear,...

Facial Expression Recognition (FER) is a classification task that points to face variants. Hence, there are certain affinity features between facial expressions, receiving lit-

The DISFA dataset is a publicly available spontaneous facial expression dataset which contains 27 subjects (12 females, and 15 males) aged in between [18, 50] and ethnically 3...

BP4D is a large-scale spontaneous facial expression database containing 41 subjects with 23 women and 18 men, each of whom is associated with 8 video-recorded tasks.