University of Utrecht researcher Itir Onal Ertugrul, University of Miami researchers Yeojin Amy Ahn and Daniel Messinger along with others published a study on automated facial action recognition in infants – which focuses on training Action Unit detectors.
Automated detection of facial action units in infants is challenging. Infant faces have different proportions, less texture, fewer wrinkles and furrows, and unique facial actions relative to adults. For these and related reasons, action unit (AU) detectors that are trained on adult faces may generalize poorly to infant faces. To train and test AU detectors for infant faces, we trained convolutional neural networks (CNN) in adult video databases and fine-tuned these networks in two large, manually annotated, infant video databases that differ in context, head pose, illumination, video resolution, and infant age. AUs were those central to expression of positive and negative emotion. AU detectors trained in infants greatly outperformed ones trained previously in adults.
Training AU detectors across infant databases afforded greater robustness to between-database differences than did training database specific AU detectors and outperformed previous state-of-the-art in infant AU detection. The resulting AU detection system, which we refer to as Infant AFAR (Automated Facial Action Recognition), is available to the research community for further testing and applications in infant emotion, social interaction, and related topics.
Onal Ertugrul I, Ahn YA, Bilalpur M, Messinger DS, Speltz ML, Cohn JF. Infant AFAR: Automated facial action recognition in infants. Behav Res Methods. 2022 May 10:10.3758/s13428-022-01863-y. doi: 10.3758/s13428-022-01863-y. Epub ahead of print. PMID: 35538295; PMCID: PMC9646921.