2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (2017)
Washington, DC, DC, USA
May 30, 2017 to June 3, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FG.2017.37
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.
M. Madadi, S. Escalera, A. Carruesco, C. Andujar, X. Baro and J. Gonzalez, "Occlusion Aware Hand Pose Recovery from Sequences of Depth Images," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)(FG), Washington, DC, DC, USA, 2017, pp. 230-237.