אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

We employ a geometric framework to extend the concept of data normalization to the domain of functions that lie on manifolds. We pose normalization in this context as an embedding of all examples into manifolds nearly isometric to one another. Using novel geometric tools, we propose an implementation for the case of discretized functions on triangulated two-dimensional meshes.

We apply the proposed geometric normalization technique to the task of automatic Action Unit (AU) detection. This problem has received much attention in the literature ever since Ekman and Friesen introduced the Facial Action Coding System (FACS) in an attempt to codify facial expressions via modular components called Action Units. By normalizing cross-subject examples to a common template face, we are able to improve the results of state-of-the-art AU detection algorithms.