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Exploration of factors affecting webcam-based automated gaze coding.

Hiromichi Hagihara1,2,3,4, Lorijn Zaadnoordijk5, Rhodri Cusack5

  • 1Graduate School of Human Sciences, Osaka University 1-2 Yamadaoka, Suita-shi Osaka, 565-0871, Japan. hiromichi.h@gmail.com.

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Summary

Online experiments enhance behavioral research. This study created a webcam dataset to improve automatic gaze classification accuracy for infants and adults, addressing noise factors in home-based studies.

Keywords:
Automated gaze codingData qualityOnline experimentOpen datasetWebcam video data

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Area of Science:

  • Behavioral research
  • Developmental psychology
  • Human-computer interaction

Background:

  • Online experiments offer benefits like larger sample sizes and diverse populations for behavioral research.
  • Gaze behavior is a key outcome measure in infant studies, but manual coding is labor-intensive.
  • Automatic gaze classification from webcam data could streamline infant research but faces challenges from uncontrolled environments.

Purpose of the Study:

  • To create a controlled adult webcam dataset replicating noise factors from infant online studies.
  • To evaluate the impact of specific noise factors on automated face and gaze detection algorithms.
  • To assess the feasibility of facial anonymization for data sharing in online behavioral research.

Main Methods:

  • Systematically varied factors: left-right offset, camera distance, facial rotation, and lighting direction.
  • Utilized two state-of-the-art classification algorithms (iCatcher+ and OWLET).
  • Tested the effect of morphing faces to be unidentifiable on algorithm performance.

Main Results:

  • Facial detection was most sensitive to lighting conditions.
  • Gaze coding accuracy was consistently impacted by camera distance and lighting.
  • Facial anonymization did not significantly degrade performance, suggesting its utility for data transparency.

Conclusions:

  • Camera distance and lighting are critical factors affecting gaze coding accuracy in online studies.
  • Facial anonymization is a viable option for sharing online behavioral data.
  • The developed dataset and findings will enhance the robustness of online testing for developmental psychology research.