Biography:Michel Valstar (http://www.cs.nott.ac.uk/~pszmv) is an associate professor at the University of Nottingham, and member of both the Computer Vision and Mixed Reality Labs. He received his masters' degree in Electrical Engineering at Delft University of Technology in 2005 and his PhD in computer science at Imperial College London in 2008, and he was a Visiting Researcher at MIT's Media Lab. He works in the fields of computer vision and pattern recognition, where his main interest is in automatic recognition of human behaviour, specialising in the analysis of facial expressions. He is the founder of the facial expression recognition challenges (FERA 2011/2015/2017), and the Audio-Visual Emotion recognition Challenge series (AVEC 2011-2017). He is the coordinator of the EU Horizon2020 project ARIA-VALUSPA, which will build the next generation virtual humans, deputy director of the 6M£ Biomedical Research Centre's Mental Health and Technology theme, and recipient of Melinda & Bill Gates Foundation funding to help premature babies survive in the developing world, which won the FG 2017 best paper award. His work has received popular press coverage in, among others, Science Magazine, The Guardian, New Scientist and on BBC Radio. He has published over 50 peer-reviewed papers at venues including PAMI, CVPR, ICCV, SMC-Cybernetics, and Transactions on Affective Computing (h-index 32, >5500 citations).
Title:The Computational Face - Facial Expression Recognition and its Application to Monitoring Medical Conditions
In this talk I will present recent advances in Facial Expression Analysis made by my team at the University of Nottingham. In particular, I will present work on the following topics, all of which will be included in my upcoming book 'The Computational Face':
1). Facial Point Localisation/Face Alignment - discussing our ground-breaking work on direct-diplacement based point detection and incremental continuous cascaded regression, published in TPAMI 2017
2). Facial Expression Analysis - our latest facial expression recognition research using Multi-task learning, and dynamic deep learning to create systems that can easily be adapted to new application areas to attain state of the art performance.
3). Behaviomedics - a novel area of using affective computing and social signal processing to help diagnose, monitor, and treat medical conditions that alter expressive behaviour, including recent work on automatic depression detection.