Real time feature-based facial tracking using Lie algebras

We have developed a novel human facial tracking system that operates in real time at a video frame rate without needing any special hardware. The approach is based on the use of Lie algebra, and uses three-dimensional feature points on the targeted human face. It is assumed that the roughly estimated facial model (relative coordinates of the three-dimensional feature points) is known. First, the initial feature positions of the face are determined using a model fitting technique. Then, the tracking is operated by the following sequence: (1) capture the new video frame and render feature points to the image plane; (2) search for new positions of the feature points on the image plane; (3) get the Euclidean matrix from the moving vector and the three-dimensional information for the points; and (4) rotate and translate the feature points by using the Euclidean matrix, and render the new points on the image plane. The key algorithm of this tracker is to estimate the Euclidean matrix by using a least square technique based on Lie algebra. The resulting tracker performed very well on the task of tracking a human face.

An in computo investigation of the Landau-Kleffner syndrome

We describe a computational model of the thalamus and the cortex able to reproduce some essential epileptiform features commonly observed in the Landau-Kleffner syndrome. Investigation with this realistic model leads us to the formulation of a cellular mechanism that could be responsible for the epileptic discharges occuring with this severe syndrome. Understanding this mechanism is of prime importance for developing new therapeutical strategies. © 2007 IEEE.