Using automatic face analysis to score infant behaviour from video collected online |
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| Institution: | 1. Trinity College Dublin, Dublin, Ireland;2. Massachusetts Institute of Technology, Cambridge, MA, USA;1. Department of Psychology, Neuroscience and Behaviour, McMaster University, Canada;2. Offord Centre for Child Studies, Canada;1. Institute for Education, Upbringing, and Care in Childhood | Rheinland-Pfalz, Department of Social Sciences, University of Applied Sciences Koblenz, D-56075 Koblenz, Germany;2. Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455, USA;3. Department of Developmental Psychology, University of Bern, CH-3012 Bern, Switzerland;1. Developmental Psychology, University of Göttingen, Germany;2. Developmental Psychology, University of Hamburg, Germany;1. Utrecht University, Utrecht institute of Linguistics OTS (UiL OTS), Trans 10, 3512 JK Utrecht, the Netherlands;2. Utrecht University, Department of Methodology and Statistics, Padualaan 14, 3584 CH Utrecht, the Netherlands;3. University of Amsterdam Research Institute of Child, Development and Education (RICDE), P.O. Box 15780, 1001 NG, Amsterdam, the Netherlands;1. Department of Human Development, Washington State University, USA;2. Department of Psychology, Washington State University, USA |
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| Abstract: | Online testing of infants by recording video with a webcam has the potential to improve the replicability of developmental studies by facilitating larger sample sizes and by allowing methods (including recruitment) to be specified in code. However, the recorded video still needs to be manually scored. This labour-intensive process puts downward pressure on sample sizes and requires subjective judgements that may not be reproducible in a different laboratory. Here we present the first fully automatic pipeline, using a face analysis software-as-a-service and a discriminant-analysis classifier to score infant videos acquired online. We compare human and machine performance for looking time and preferential looking paradigms; machine performance demonstrates a promising proof of principle for looking time and is above chance in classifying preferential looking. Additionally, we studied the characteristics of the video and the child that influenced automated scoring, so that future studies can acquire data that maximises the performance of automatic gaze coding and/or focus on improving automatic coding for particularly challenging data. We believe this technology has great promise for developmental science. |
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| Keywords: | Face detection Machine vision Looking time Preferential looking Webcam |
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