People are highly adept at perceiving others, despite the complexity of social perception. After extremely brief exposure or highly degraded visual input, people can identify faces, races and gender, recognize emotional expressions and make a variety of social judgments about aggressiveness, trustworthiness and sexual orientation. How do people manage these marvellous perceptual and cognitive feats? This course aims to synthesize research from multiple disciplines on how faces are perceived and represented in cognitive and computational systems. It will do so by drawing on insights from psychology and artificial intelligence in order to understand the foundations of the social perception of faces.Armed with a social face perception model, the course will introduce machine learning techniques that can be used to classify facial emotions. During the course, you will use prefabricated software blocks and programs to develop a mobile app that is capable of classifying real-time facial emotions.Typical uses for this technique include:• Medical applications (e.g. an app that can help autistic children learn to recognize emotions, or a game that can help GTS patients learn to suppress tics);• Domestic applications (e.g. emotion recognition sensors that can adjust ambient lighting and music styles); • Entertainment applications (e.g. using emotion recognition to add a new dimension to home entertainment)

Period
11-08-2014 - 15-08-2014 (1 weeks)

Target group
The course is specifically aimed at two groups:1.Psychology students with an affinity for research and an interest in computer applications2.Artificial intelligence students with an interest in human cognition

Course aim
After this course you will be able to: 1. Apply theoretical and practical knowledge about human emotions2. Use computer programs to model social face perception 3. Master the basic principles of some popular machine learning algorithms4. Construct a working app (for phone or tablet) that is able to classify facial emotions



Credits
2.0 ECTS credits

Course fee
EUR 400[Convert to USD]The course fee includes the registration fee, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities. The fee does not include accommodation, travel costs, dinner, insurance and other costs. Discounts10% discount for early bird applicants. The early bird deadline is 1 April 2014.15% discount for students and PhD candidates from Radboud University and partner universities

Course leader
Dr. F.A. (Franc) Grootjen, Assistant Professor Artificial Intelligence Dr R. (Ron) Dotsch, Assistant Professor Research Master Behavioural Science, Radboud University

Radboud University
Address: P.O Box 9102 Nijmegen
Postal code: 6500 HC
City: Nijmegen
Country: Netherlands
Website: http://www.ru.nl/radboudsummerschool/
E-mail: radboudsummerschool@ru.nl
Phone: +31 (0)24 8187706