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Evaluating Open-Source Solutions for Computerized Inference of Infant Facial Affect.

Martin Lund Trinhammer1,2, Ida Egmose3, Marianne Thode Krogh3

  • 1Audio-Visual Computing, Section of Data Science, IT University of Copenhagen, Copenhagen S, Denmark.

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|February 24, 2026
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This study introduces PyAFAR, an open-source tool for analyzing infant facial expressions. It accurately classifies infant affect, matching commercial software performance and aiding developmental research.

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Area of Science:

  • Developmental Psychology
  • Computer Vision
  • Affective Computing

Background:

  • Infant facial expressions are crucial for understanding well-being and social development.
  • Manual coding of infant affect is time-consuming; computational methods are needed.
  • Existing infant affect analysis tools are limited, with only commercial options available.

Purpose of the Study:

  • To evaluate the efficacy of the open-source infant-native action unit (AU) detection library, PyAFAR (Python-based Automated Facial Action Recognition).
  • To classify infant facial affect (negative, neutral, positive) using PyAFAR-derived AUs and machine learning models.
  • To address the gap in open-source computational tools for infant affect analysis.

Main Methods:

  • Utilized PyAFAR to detect action units (AUs) from facial expressions of 71 four-month-old infants.
  • Manually annotated infant facial expressions frame-by-frame using the Infant Facial Affect (IFA) coding scheme.
  • Employed XGBoost and Bayesian filtering for multiclass and binary classification of affect based on AU features.

Main Results:

  • PyAFAR-derived AUs with XGBoost achieved AUC scores of 0.78 (positive vs. neutral) and 0.76 (positive vs. negative).
  • Performance was comparable to the commercial Baby FaceReader 9, considering study variations.
  • Demonstrated the potential of supervised learning for infant facial affect analysis.

Conclusions:

  • The open-source PyAFAR library shows significant promise for computational infant affect analysis.
  • PyAFAR offers a viable, open-source alternative to commercial software for researchers and clinicians.
  • Future PyAFAR development could enhance accuracy by incorporating additional AUs for infant-specific expressions.