Efficient appearance-based tracking with illumination changes and face expressions. We introduce a subspace representation of face appearance which separates facial expressions from illumination variations. The appearance of a face is represented by the addition of two independent linear subspaces modelling facial expressions and illumination. This simple model enables us to train the system with no manual intervention. We also introduce an efficient procedure for fitting this model, which can be used for tracking a human face in real-time.

Efficient 3d nonrigid tracking. Efficient incremental image alignment is a topic of renewed interest in the computer vision community because of its applications in model fitting and model-based object tracking. We are working in efficient solutions to the 3D tracking of a head performing face expressions under changing illumination conditions.

Efficient appearance-based tracking We have developed an efficient way of minimizing the eigentracking for nonrigid motion estimation. It is based on the precomputation of motion templates to save on-line computation. It allows as to estimate appearance (PCA coefficients) and motion in real-time.

SSD based 3D tracking. We have developed an algorithm for tracking a rigid object based on a piecewise planar model. The tracking is performed using a single incremental SSD-based tracker. The main feature of the approach presented is that it can track a rigid set of arbitrarily small patches all of which could not be individually tracked.

SSD based tracking. Planar tracking can be used for face tracking. We have extended a well known framework for planar tracking (see [Hager98]) with a projective motion model. Additionally, using a calibrated camera, it is possible to estimate the 3D pose of the planar object. We have also developed a procedure to select the most informative pixels of the target template image for faster tracking.

Background Maintenance + Motion detection. Motion is one of the best visual cues to focus the attention of a tracking system. We have developed a background maintenance algorithm that incrementally generates the background image of a scene, from an image sequence. The background generated can be used to detect motion in the image.

Colour Based Tracking. Tracking using colour is difficult when sudden light colour changes take place. We have extented a well known colour constancy algorithm, Grey World, to deal with such situations. The result is more robust than widely used RGB-normalisation, although it is not perfect either.

Robust face tracking. All the algorithms based on a simple visual cues fail in some circumstances. The key idea is to use this simple algorithms together in order to get robustness. All this algorithms should be "orthogonal" in the sense of having different fail conditions.