TY - JOUR KW - Performance Creative Evaluation KW - Multimodal Affective Feature KW - Multimedia Acquisition KW - Data-driven KW - Affective Acceptance AU - Yufeng Wu AU - Longfei Zhang AU - Gangyi Ding AU - Tong Xue AU - Fuquan Zhang AB - Performance creative evaluation can be achieved through affective data, and the use of affective featuresto evaluate performance creative is a new research trend. This paper proposes a “Performance Creative—Multimodal Affective (PC-MulAff)” model based on the multimodal affective features for performance creative evaluation. The multimedia data acquisition equipment is used to collect the physiological data of the audience, including the multimodal affective data such as the facial expression, heart rate and eye movement. Calculate affective features of multimodal data combined with director annotation, and defined “Performance Creative—Affective Acceptance (PC-Acc)” based on multimodal affective features to evaluate the quality of performance creative. This paper verifies the PC-MulAff model on different performance data sets. The experimental results show that the PC-MulAff model shows high evaluation quality in different performance forms. In the creative evaluation of dance performance, the accuracy of the model is 7.44% and 13.95% higher than that of the single textual and single video evaluation. IS - Special Issue on Current Trends in Intelligent Multimedia Processing Systems M1 - 7 N2 - Performance creative evaluation can be achieved through affective data, and the use of affective featuresto evaluate performance creative is a new research trend. This paper proposes a “Performance Creative—Multimodal Affective (PC-MulAff)” model based on the multimodal affective features for performance creative evaluation. The multimedia data acquisition equipment is used to collect the physiological data of the audience, including the multimodal affective data such as the facial expression, heart rate and eye movement. Calculate affective features of multimodal data combined with director annotation, and defined “Performance Creative—Affective Acceptance (PC-Acc)” based on multimodal affective features to evaluate the quality of performance creative. This paper verifies the PC-MulAff model on different performance data sets. The experimental results show that the PC-MulAff model shows high evaluation quality in different performance forms. In the creative evaluation of dance performance, the accuracy of the model is 7.44% and 13.95% higher than that of the single textual and single video evaluation. PY - 2021 SP - 90 EP - 100 T2 - International Journal of Interactive Multimedia and Artificial Intelligence TI - Modeling of Performance Creative Evaluation Driven by Multimodal Affective Data UR - https://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_9.pdf VL - 6 SN - 1989-1660 ER -