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<p>Video-based emotion change estimation using spatiotemporal features is an emerging area of research, enabling various practical applications. Existing methods often overlook continual analysis of the changes in emotions during spontaneous conversations. In this study, considering a set of spatial and temporal attributes, a novel one-dimensional <i>weighted emotion model</i> is proposed to accurately scale the emotion intensity levels. Further, a tri-stream deep framework, called <i>TriCe</i>, is proposed to estimate the emotion changes using three complementary modal information from videos, namely key-frame, video and voice. In TriCe, each modal information is extracted separately and combined using an adaptive fusion technique, which is then used to recognize the emotion level. Experimental results on a newly constructed benchmark emotion dataset demonstrate that our TriCe framework is feasible in estimating emotion changes. Comparison with other existing works demonstrates that the TriCe sets the benchmark for emotion estimation.</p>

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TriCe: Tri-stream video-based emotion change estimation framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月20日

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  • Selvarajah Thuseethan,
  • Sutharshan Rajasegarar,
  • John Yearwood,
  • Bharanidharan Shanmugam,
  • Yakub Sebastian

摘要

Video-based emotion change estimation using spatiotemporal features is an emerging area of research, enabling various practical applications. Existing methods often overlook continual analysis of the changes in emotions during spontaneous conversations. In this study, considering a set of spatial and temporal attributes, a novel one-dimensional weighted emotion model is proposed to accurately scale the emotion intensity levels. Further, a tri-stream deep framework, called TriCe, is proposed to estimate the emotion changes using three complementary modal information from videos, namely key-frame, video and voice. In TriCe, each modal information is extracted separately and combined using an adaptive fusion technique, which is then used to recognize the emotion level. Experimental results on a newly constructed benchmark emotion dataset demonstrate that our TriCe framework is feasible in estimating emotion changes. Comparison with other existing works demonstrates that the TriCe sets the benchmark for emotion estimation.