Continuous Affect Estimation

AffectNet is a large facial expression dataset with around 0.4 million images manually labeled for the presence of eight (neutral, happy, angry, sad, fear, surprise, disgust, contempt) facial expressions along with the intensity of valence and arousal. Source: Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition Image Source: http://mohammadmahoor.com/affectnet/

We present a database for research on affect, personality traits and mood by means of neuro-physiological signals. Different to other databases, we elicited affect using both short and long videos in two configurations, one with individual viewers and one with groups of viewers. The database allows the multimodal study of the affective responses of individuals in relation to their personality and mood, and the analysis of how these responses are affected by (i) the individual/group configuration, and (ii) the duration of the videos (short vs long). The data is collected in two experimental settings. In the first one, 40 participants watched 16 short emotional videos while they were alone. In the second one, the same participants watched 4 long videos, some of them alone and the rest in groups. In both settings, the participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR), were recorded using wearable sensors. Frontal, full-body and depth videos were also recorded. Participants have been profiled for personality using the Big-five personality traits, and for mood with the baseline Positive Affect and Negative Affect Schedules. Participants emotions have been annotated with both, self-assessment of affective levels (valence, arousal, control, familiarity, like/dislike, and selection of basic emotion) felt by the participants during the first experiment, and external-assessment of participants' levels of valence and arousal for both experiments. We present a detailed correlation analysis that includes correlations between self-assessment and external-assessment of affect, between valence and arousal elicited by short and long videos on individuals and groups, as well as, between personality, mood, social context, and affect dimensions. We also present baseline methods and results for single-trial classification of valence and arousal, and for single-trial classification of personality traits, mood and social context (alone vs group), using EEG, GSR and ECG and fusion of modalities for both experiments.

We present a dataset for the analysis of human affective states using functional near-infrared spectroscopy (fNIRS). Data were recorded from thirty-one participants who engaged in two tasks. In the emotional perception task the participants passively viewed images sampled from the standard international affective picture system database, which provided ground-truth valence and arousal annotation for the stimuli. In the affective imagery task the participants actively imagined emotional scenarios followed by rating these for subjective valence and arousal. Correlates between the fNIRS signal and the valence-arousal ratings were investigated to estimate the validity of the dataset. Source-code and summaries are provided for a processing pipeline, brain activity group analysis, and estimating baseline classification performance. For classification, prediction experiments are conducted for single-trial 4-class classification of arousal and valence as well as cross-participant classifications, and comparisons between high and low arousal variants of the valence prediction tasks. Finally, classification results are presented for subject-specific and cross-participant models. The dataset is made publicly available to encourage research on affective decoding and downstream applications using fNIRS data. Paper with more information and to be cited upon usage: https://ieeexplore.ieee.org/abstract/document/10286101