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Summary
This summary is machine-generated.

This study introduces a novel video vision transformer model for accurate emotion recognition from facial expressions in videos. The approach effectively extracts emotion cues without multimodal data, outperforming existing methods.

Keywords:
deep learningfacial expression recognition (FER)transformervideo

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Emotion recognition from real-world videos is challenging due to data limitations like missing audio or subtitles.
  • Multimodal models struggle with diverse real-world video content, and individual identity variations complicate facial expression analysis.

Purpose of the Study:

  • To propose a transformation model for robust emotion recognition from facial expression sequences in videos.
  • To overcome the limitations of multimodal approaches by focusing solely on visual facial expression data.

Main Methods:

  • Employed a video vision transformer to focus on facial expression sequences, extracting information independent of individual identities.
  • Utilized mixed-token embedding for facial expression sequences, augmented via various methods, into a single data representation.
  • Incorporated spatial and temporal encoders, with proposed temporal position embedding to capture frame relationships.

Main Results:

  • The proposed model effectively understood and extracted facial expression information from video sequences.
  • Demonstrated superior performance compared to conventional methods on two distinct emotion recognition datasets.
  • Successfully addressed challenges posed by diverse identities and missing data in real-world videos.

Conclusions:

  • The developed transformation model offers a superior approach to emotion recognition in challenging real-world video scenarios.
  • Focusing on facial expression sequences via a video vision transformer provides a viable alternative to complex multimodal fusion.
  • The method enhances the accuracy and applicability of emotion recognition systems for unconstrained video data.