%0 Conference Proceedings
%T Occlusion Aware Hand Pose Recovery from Sequences of Depth Images
%A Meysam Madadi
%A Sergio Escalera
%A Alex Carruesco
%A Carlos Andujar
%A Xavier Baro
%A Jordi Gonzalez
%B 12th IEEE International Conference on Automatic Face and Gesture Recognition
%D 2017
%F Meysam Madadi2017
%O HUPBA; ISE; 602.143; 600.098; 600.119
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2970), last updated on Fri, 21 Jan 2022 15:03:30 +0100
%X State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.
%U http://refbase.cvc.uab.es/files/MEC2017.pdf
%U http://dx.doi.org/10.1109/FG.2017.37