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Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Zohaib Ahmad Khan1, Yuanqing Xia1, Khursheed Aurangzeb2

  • 1School of Automation, Beijing Institute of Technology, Beijing, China.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an attention-based transformer model for emotion detection using handwriting and drawing analysis. The novel method achieves 92.64% accuracy, advancing emotion recognition capabilities.

Keywords:
Behavioral biometricsEmotion detectionEmotional intelligenceEmotional state recognitionHandwriting/Drawing analysisHuman-computer InteractionTransformer model

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Area of Science:

  • Psychology
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion detection (ED) traditionally analyzes facial expressions, voice, and physiology.
  • Behavioral analysis offers an alternative, focusing on observable actions.
  • Handwriting and drawing integrate motor and cognitive processes, providing unique behavioral cues for emotion.

Purpose of the Study:

  • To propose an innovative attention-based transformer model for emotion detection.
  • To leverage behavioral metrics from handwriting and drawing for emotional state identification.
  • To advance ED into the domain of fine motor skills and artistic expression.

Main Methods:

  • Utilized an attention-based transformer model for analyzing handwriting and drawing samples.
  • Processed stroke data by embedding each point into a high-dimensional vector space.
  • Employed self-attentional processes to identify key patterns and long-range correlations in the input sequence.

Main Results:

  • Achieved state-of-the-art performance with 92.64% accuracy on the EMOTHAW benchmark dataset.
  • Demonstrated the model's effectiveness in capturing complex patterns in handwriting and drawing.
  • Showcased the superiority of the attention-based transformer over conventional recurrent neural networks (RNNs) in this task.

Conclusions:

  • The proposed attention-based transformer model is highly effective for emotion detection using handwriting and drawing.
  • This approach offers enhanced capabilities for capturing long-range dependencies in behavioral data.
  • The findings represent a significant advancement in the field of emotion detection through behavioral analysis.