This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.
Similar to the MOSI dataset, the EmoReact @cite_9 also contains videos from Youtube. The special feature of this dataset is that it contains videos from children of the Kids React Youtube channel. The dataset is annotated with 17 different affective states: the six basic emotions (Happiness, Sadness, Surprise, Fear, Disgust and Anger), neutral, valence intensity, curiosity, uncertainty, excitement, attentiveness, exploration, confusion, anxiety, embarrassment and frustration. This dataset was also annotated using the Amazon Mechanical Turk tool, with the annotators using a 1-4 Likert scale for each emotion, where 1 shows the absence of emotion and 4 shows the intense presence of the emotion. This dataset presents a general annotation scheme, and although it does not have a dimensional annotation, it has the intensity of different emotion categories. The videos contain children only, and thus this corpus is highly biased to how children express emotions. Also, the lack of contextual information makes it more suitable to be used on instance-based emotion classification.
@cite_9
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Automatic emotion recognition plays a central role in the technologies underlying social robots, affect-sensitive human computer interaction design and affect-aware tutors. Although there has been a considerable amount of research on automatic emotion recognition in adults, emotion recognition in children has been understudied. This problem is more challenging as children tend to fidget and move around more than adults, leading to more self-occlusions and non-frontal head poses. Also, the lack of publicly available datasets for children with annotated emotion labels leads most researchers to focus on adults. In this paper, we introduce a newly collected multimodal emotion dataset of children between the ages of four and fourteen years old. The dataset contains 1102 audio-visual clips annotated for 17 different emotional states: six basic emotions, neutral, valence and nine complex emotions including curiosity, uncertainty and frustration. Our experiments compare unimodal and multimodal emotion recognition baseline models to enable future research on this topic. Finally, we present a detailed analysis of the most indicative behavioral cues for emotion recognition in children.