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 approximately independent linear subspaces modelling facial expressions and illumination respectively. The independence assumption notably simplifies the training of the system. We only require two image sequences. One in which one facial expression is subject to all possible illuminations and another in which the face, under one illumination, performs all facial expressions. 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.

Abstract not found

Abstract not found

Pixelbrightnessvariationsinanimagesequencedependbothontheobjects’ surface reflectance and on the motion of the camera and object. In the case of rigid shapes some proposed models have been very successful explaining the relation among these strongly coupled components. On the other hand, shapes which deform pose new challenges since the relation between pixel brightness variation with non-rigid motion is not yet clear. In this paper, we introduce a new model which describes brightness variations with two independentcomponentsrepresentedaslinearbasisshapes. Lightinginfluenceis representedintermsofSphericalHarmonicsandnon-rigidmotionasalinear model which represents image coordinates displacement. We then propose an efficient procedure for the estimation of this image model in two distinct steps. First, shape normals and albedo are estimated using standard photometric stereo on a sequence with varying lighting and no deformable motion. Then, given the knowledge of the object’s shape normals and albedo, we efficiently compute the 2D coordinates bases by minimizing image pixel residuals over an image sequence with constant lighting and only non-rigid motion. Experiments on real tests show the effectiveness of our approach in a facemodelling context. 1