Automatic Prediction of Group Cohesiveness in Images

نویسندگان

چکیده

This article discusses the prediction of cohesiveness a group people in images. The is an essential indicator emotional state, structure, and success group. We study factors that influence perception group-level cohesion propose methods for estimating human-perceived on scale. To identify visual cues (attributes) cohesion, we conducted user survey. Image analysis performed at via multi-task convolutional neural network. A capsule network explored analyzing contribution facial expressions members predicting Group Cohesion Score (GCS). add GCS to Affect database ‘GAF-Cohesion database’. proposed model performs well achieves near human-level performance group's score. It interesting note as attribute, when jointly trained emotion prediction, helps increasing later task. suggests are correlated. Further, investigate effect face-level similarity, body pose subset task automatic perception.

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ژورنال

عنوان ژورنال: IEEE Transactions on Affective Computing

سال: 2022

ISSN: ['1949-3045', '2371-9850']

DOI: https://doi.org/10.1109/taffc.2020.3026095