Computerized Analysis of Face Emotion Recognition Skills and Facial Behaviors in Children with Attention Deficit Hyperactivity Disorder

Abstract

Objective: The aim of this study was to investigate the facial expressions of children diagnosed with attention deficit hyperactivity disorder (ADHD) using computerized facial analysis and to examine their emotion recognition abilities.

Methods: A total of 56 children with ADHD and 45 control subjects aged 6-12 years were included. The Diagnostic Analysis of Nonverbal Expressions-2 (DANVA) was used to measure the participants’ emotion recognition abilities. One group of participants watched animated film scenes lasting an average of 7 minutes, and their facial behaviors were recorded on video. OpenFace software was used for video analysis. Support Vector Machines (SVM), naive Bayes, and logistic regression machine learning methods were used to distinguish between the data of the ADHD and control groups.

Results: The significant difference found in DANVA total scores indicating poorer emotion recognition skills in ADHD was not significant when intelligence levels were controlled. Children with inattention as the primary symptom made significantly more errors in emotion recognition from posture and total scores in DANVA child faces and overall compared to the other groups. According to computerized facial analysis, Video 1, which predominantly featured fear and anger emotions, was the most distinctive video for both healthy controls and the ADHD group. When analyzing AU units, AU12 (lip corner pulling), AU07 (eyelid raising), AU09 (nose wrinkling), AU45 (eye blinking), and AU06 (cheek raising) were the most distinctive features.

Conclusion: Emotion recognition levels differed among ADHD cases according to clinical subtypes and comorbid psychiatric disorders. The most significant difference between the ADHD and control groups during emotion-containing video viewing was observed while watching sad videos. The findings of this study can be considered promising for the diagnostic validity of machine learning methods in ADHD, oneof the most common neurodevelopmental disorders.

Cite this article as: Karabekiroglu K, Usta MB, Kesim N, Şahin İ, Ayyıldız M. Computerized analysis of face emotion recognition skills and facial behaviors in children with attention deficit hyperactivity disorder. Neuropsychiatr Invest. 2026, 64, 0054, doi:10.5152/NeuropsychiatricInvest.2026.25054.