Research Article Computer Vision Tools for Low-Cost and Noninvasive Measurement of Autism-Related Behaviors in Infants Jordan Hashemi, 1 Mariano Tepper, 1 Thiago Vallin Spina, 2 Amy Esler, 3 Vassilios Morellas, 4 Nikolaos Papanikolopoulos, 4 Helen Egger, 5 Geraldine Dawson, 6 and Guillermo Sapiro 7 1 Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA 2 Institute of Computing, University of Campinas, 13083 Campinas, SP, Brazil 3 Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA 4 Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA 5 Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 22708, USA 6 Department of Psychiatry and Behavioral Sciences and School of Medicine, Duke University, Durham, NC 27708, USA 7 Department of Electrical and Computer Engineering, Department of Computer Science, and Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA Correspondence should be addressed to Jordan Hashemi; jordan.hashemi33@gmail.com Received 19 November 2013; Revised 30 April 2014; Accepted 13 May 2014; Published 22 June 2014 Academic Editor: Herbert Roeyers Copyright © 2014 Jordan Hashemi et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. he early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the irst year of life. Many of these studies involve extensive frame-by-frame video observation and analysis of a child’s natural behavior. Although nonintrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are burdensome for clinical and large population research purposes. his work is a irst milestone in a long-term project on non-invasive early observation of children in order to aid in risk detection and research of neurodevelopmental disorders. We focus on providing low-cost computer vision tools to measure and identify ASD behavioral signs based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure responses to general ASD risk assessment tasks and activities outlined by the AOSI which assess visual attention by tracking facial features. We show results, including comparisons with expert and nonexpert clinicians, which demonstrate that the proposed computer vision tools can capture critical behavioral observations and potentially augment the clinician’s behavioral observations obtained from real in-clinic assessments. 1. Introduction he analysis of children’s natural behavior is of key impor- tance for the early detection of developmental disorders such as autism spectrum disorder (ASD). For example, several studies have revealed behaviors indicative of ASD in early home videos of children that were later diagnosed with ASD [1–5]. hese studies involved video recording infant behavior and then coding and analyzing the data a posteriori, using frame-by-frame viewing by an observer who typically trains for several weeks to achieve interrater reliability. Hours of labor are required, thereby making such analyses burdensome for clinical settings as well as for big data studies aiming at the discovery or improvement of behavioral markers. While clinical tools for early screening of ASD are available, they require administration and interpretation by specialists. Many families in low resource communities lack easy access to specialists in ASD. his work examines the potential beneits that computer vision can provide for research in early detection of ASD risk behaviors. It is a irst Hindawi Publishing Corporation Autism Research and Treatment Volume 2014, Article ID 935686, 12 pages http://dx.doi.org/10.1155/2014/935686