Deep Learning for Facial Expression Analysis
Facial expression analysis has becoming a popular research topic in recent years due to multidiscipline collective efforts from researchers in computer science, psychology, and cognitive science. Artificial intelligence has made significant contribution for facial expression analysis that can be used for the design of advanced human machine interaction system, intelligent robots and computer games. It can also be used for human mental health analysis such as dementia, autism and clinical diagnosis application such as shoulder pain and low back pain. In my talk, I will present what we have developed on how to build the automatic emotion analysis systems and how deep learning have been applied in these systems with improved performance.
Dr Hongying Meng is a lecturer (assistant professor) in Department of Electronic and Computer Engineering at Brunel University London, UK. He is also a member of Institute of Environment, Health and Societies, and Human Centred Design Institute (HCDI) there. He has a wide research interests including digital signal processing, machine learning, human computer interaction, image processing and embedded systems. His present research focuses on image processing and machine learning (deep learning) with applications, such as facial expression analysis. He has developed two different facial expression analysis systems that won the international challenge completions AVEC2011 (http://sspnet.eu/avec2011/) and AVEC2013 (http://sspnet.eu/avec2013/) respectively.