Learning Natural Facial Expressions
Natural facial behavior can reveal information about our internal states intentions. I will describe two recent studies of machine learning on facial expression dynamics, where multi-stage learning models outperformed human observers. The first task was to distinguish genuine from faked pain. The second was to predict when a financial offer would be rejected in an economic game. My colleagues and I began a start-up company, Emotient, in 2012 to make the facial expression software commercially available. The potential for this technology is far-reaching, across fields of healthcare, education, advertising, and retail. I will wrap up the talk by describing applications in these areas.
Marian Bartlett, Ph.D. is co-Founder and Lead Scientist at Emotient, a San Diego based start-up company for automatic facial expression analysis, and Full Research Professor at University of California, San Diego. Marian is a pioneer in the field of machine learning and computer vision for face analysis. She and her colleagues developed software that automatically detects facial expressions of the seven primary emotions, as well as individual facial muscle movements, in collaboration with Paul Ekman, a founder of the science of facial behavior. The potential for this technology is far-reaching, across fields of healthcare, education, advertising, and retail. The technology was awarded best new product from CONNECT, San Diego, in 2013, and Marian was a winner of the 2014 Women Who Mean Business Award from the San Diego Business Journal. Marian received her Ph.D. from University of California, San Diego in Cognitive Science and Psychology, and her B.A. from Middlebury College in Mathematics and Computer Science. She has authored over 80 papers in scientific journals and conference proceedings, as well as a book, Face Image Analysis by Unsupervised Learning, published by Kluwer in 2001.