Detection of eye contact with deep neural networks is as accurate as human experts

• Published in: Nature communications Vol. 11; no. 1; pp. 6386 - 10
• Main Authors: Chong, Eunji, Clark-Whitney, Elysha, Southerland, Audrey, Stubbs, Elizabeth, Miller, Chanel, Ajodan, Eliana L., Silverman, Melanie R., Lord, Catherine, Rozga, Agata, Jones, Rebecca M., Rehg, James M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.12.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 706/689/477; Artificial neural networks; Autism; Autism Spectrum Disorder; Child, Preschool; Coders; Communication; Communication skills; Deep Learning; Diagnosis; Eye; Eye contact; Female; Hand; Humanities and Social Sciences; Humans; Infant; Machine Learning; Male; Models, Theoretical; multidisciplinary; Neural networks; Neural Networks, Computer; Recall; Science; Science (multidisciplinary); Social behavior

Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysis of social roles and communication skills, and for clinical screening. Estimating a subject’s looking direction is a challenging task, but eye contac...