In this research project, our focus is on developing automated computer vision techniques for measuring/recognizing spontaneous facial expressions and FACS action units and map onto expert human coding. We apply developed automated techniques to analyze emotion expressions of children with autism. Autism is a severe early childhood developmental disorder that is characterized by deficits in cognitive performance as well as progressive qualitative impairments in social interactions. In the absence of a reliable biological marker, diagnosis of this devastating disorder is still based on its clinical manifestations. One of the clinical features of autism is difficulties in appropriately creating facial expressions that reflect child’s emotions.
Related Publications:
- Yongqiang Li, Seyed Mohammad Mavadati, Mohammad H. Mahoor’s, and Qiang Ji, “A Unified Probabilistic Framework For Measuring The Intensity of Spontaneous Facial Action Units“, 10th IEEE International Conference on Automatic Face and Gesture Recognition, 2013.
- Nazanin Zaker, Mohammad H. Mahoor, Whitney I. Mattson, Daniel S. Messinger, Jeffrey F. Cohn, “Intensity Measurement of Spontaneous Facial Actions: Evaluation of Different Image Representations”, In the Proceedings of the IEEE International Conference on Development and Learning and Epigenetic Robotics, November 2012.
- Xiao Zhang and Mohammad H. Mahoor, “Task-Dependent Multi-task Multiple Kernel Learning for Facial Action Unit Detection“, Pattern Recognition, Volume 51, March 2016, Pages 187–196.
- Yongqiang Li, Seyed Mohammad Mavadati, Mohammad H. Mahoor, Yongping Zhao, Qiang Ji, “Measuring the intensity of spontaneous facial action units with dynamic Bayesian network”. Pattern Recognition 48(11): 3417-3427, 2015.