Robust parameterized component analysis: theory and applications to 2D facial appearance models
Computer Vision and Image Understanding - Special issue on Face recognition
Priority coding for video-telephony applications based on visual attention
MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Automatic foreground extraction of head shoulder images
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
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In a number of practical scenarios, such as video conferencing and visual human/computer interaction, objects that belong to a well defined class are segmented, normalized, and encoded, after which they are stored and/or transmitted, and subsequently reconstructed. The Karhunen-Loeve transform (KLT) optimally concentrates the signal power in a relatively small number of uncorrelated coefficients. Nevertheless, it implicitly assumes a multidimensional Gaussian probability model, which is typically not correct. Here we show that, in the context of video sequences of human heads, the segmentation and normalization steps result in partial symmetries which force the KLT coefficients to lie close to low-dimensional manifolds in suitably chosen high-dimensional KLT subspaces. We show how this fact can be used to track the faces robustly, and to estimate their pose. We use vector quantization to discover those manifolds, and to build a factorial code that has a substantially lower dimensionality than KLT.