רן ברויאר (מדעי המחשב, טכניון)
יום חמישי, 4.5.2017, 11:30
חדר 337, בניין טאוב למדעי המחשב
Facial expressions play a significant role in human communication and behavior. Psychologists have long studied the relationship between facial expressions and emotions. Paul Ekman et al., devised the Facial Action Coding System (FACS)
to taxonomize human facial expressions and model their behavior. The ability to recognize facial expressions automatically, enables novel applications in fields like human-computer interaction, social gaming, and psychological research.
There has been a tremendously active research in this field, with several recent papers utilizing convolutional neural networks (CNN) for feature extraction and inference. We employ CNN understanding methods to study the relation between the features these computational networks are using, the FACS and Action Units (AU). We apply these models to various tasks and tests using transfer learning, including cross-dataset validation, cross-task performance and micro-expression detection.