PT Unknown
AU Meysam Madadi
   Sergio Escalera
   Alex Carruesco
   Carlos Andujar
   Xavier Baro
   Jordi Gonzalez
TI Occlusion Aware Hand Pose Recovery from Sequences of Depth Images
BT 12th IEEE International Conference on Automatic Face and Gesture Recognition
PY 2017
DI 10.1109/FG.2017.37
AB 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.
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