The MSP-Face database is a natural emotional multimodal corpus collected from video-sharing websites. The recordings include people in front of a camera speaking about different situations from their daily life, giving their opinions. The videos consist of natural and spontaneous recordings, where the emotions are not acted or artificially elicited. The data collection protocol is flexible and scalable, and addresses key limitations of existing multimodal databases. The data was collected from multiple participants, expressing a broad range of emotions, which is not easily achieved with other data collection protocols.
A feature of the corpus is the addition of two sets. The first set includes videos that have been annotated with emotional labels using a crowd-sourcing protocol 9,370 recordings (24 hrs, 41 mins). The second set includes similar videos without emotional labels, offering the perfect infrastructure to explore semi-supervised and unsupervised machine-learning algorithms on natural emotional videos. This set has 15,011 recordings (38 hrs, 3 mins).
This corpus is annotated with emotional labels using attribute-based descriptors (activation, dominance and valence) and categorical labels (anger, happiness, sadness, disgust, surprised, fear, contempt, neutral and other). With over 62 hrs of labeled and unlabeled data, this corpus provides an important resource that complements and extends existing emotional databases. The applications that we have in mind include emotion recognition from videos, and visual agents with expressive behaviors.
The MSP-Face corpus is being recorded as part of our NSF project "RI: Small: Integrative, Semantic-Aware, Speech-Driven Models for Believable Conversational Agents with Meaningful Behaviors'" (NSF IIS: 1718944). For further information on the corpus, please read:
We plan to share this corpus with the research community in the future.
This material is based upon work supported by the National Science Foundation under Grant IIS-1453781. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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