IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Personality of Participants During Group Interactions
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Multimodal Biometrics-Based Student Attendance Measurement in Learning Management Systems
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Crowdsourced data collection of facial responses
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Please, tell me about yourself: automatic personality assessment using short self-presentations
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Proceedings of the 15th ACM on International conference on multimodal interaction
One of a kind: inferring personality impressions in meetings
Proceedings of the 15th ACM on International conference on multimodal interaction
Hi YouTube!: personality impressions and verbal content in social video
Proceedings of the 15th ACM on International conference on multimodal interaction
Inferring mood in ubiquitous conversational video
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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The advances in automatic facial expression recognition make possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-the-art facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers' personality impressions using facial expressions and the Big-Five traits. Our results are promising, specially for the case of the Extraversion impression, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video.