Automatic Human Behavior Analysis and Recognition for Research and Clinical Use

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Zakia Hammal

Senior Project Scientist, The Robotics Institute, Carnegie Mellon University

Résumé:

Nonverbal behavior is multimodal and interpersonal. In several studies, I addressed the dynamics of facial expression and head movement for emotion communication, social interaction, and clinical applications. By modeling multimodal and interpersonal communication my work seeks to inform affective computing and behavioral health informatics. In this talk, I will address some of my recent work that has addressed computational methods for affect communication in children with facial abnormalities, automatic measurement of pain intensity, and depression severity assessment. I will conclude my talk by sketching my efforts to pursue my research in using stateof-the art approaches from both AI and machine learning in building new multimodal artificial intelligence focused around understanding and modeling human emotion, physical, and cognitive states.

Biographie :

Zakia Hammal is a senior project scientist at the Robotics Institute at Carnegie Mellon University. Her training is in computer vision, machine learning, and signal and image processing. Through her early work, she developed leading approaches for automatic tracking of face motion and the detection of the occurrence and timing of facial expressions. Much of her recent work has addressed computational methods for automatic detection of pain and pain intensity, automatic assessment of treatment outcomes in psychiatric disorders, automatic measurement of behavioral markers in autism spectrum disorder, and modelling the dynamics of nonverbal behavior in social interaction. She has made extensive and sustained contributions to the social and organizational betterment of affective computing. She organized successful workshops in Interpersonal Synchrony and Influence (INTERPERSONAL at ICMI 2015), and in Face and Gesture Analysis for Health Informatics (FGAHI at CVPR 2019, FG 2018). To promote the critical importance of context in affect recognition, she leads a series of six successful Context-Based Affect Recognition workshops at premier IEEE conferences in computer vision, affective computing, social communication, and multimedia (CBAR at FG 2019, ACII 2017, CVPR 2016, FG 2015, ACII 2013, and SocialCom 2012). She served as Publication Chair of ACM ICMI 2014, Area Chair of IEEE FG 2017 and FG 2019, and served as mentor in several doctoral consortia (FG 2013, ACII 2015, FG 2018). Her honors include an outstanding paper award at ACM ICMI 2012, Best paper ward at IEEE ACII 2015, and Outstanding Reviewer Award at IEEE FG 2015.

Website: https://www.ri.cmu.edu/personal-pages/ZakiaHammal/.