Person Identification Using Pose-Based Hough Forests from Skeletal Action Sequence

Ju Yong CHANG  Ji Young PARK  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.3   pp.767-777
Publication Date: 2018/03/01
Publicized: 2017/12/04
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7215
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
person identification,  3D human pose,  Hough forests,  

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The present study considers an action-based person identification problem, in which an input action sequence consists of 3D skeletal data from multiple frames. Unlike previous approaches, the type of action is not pre-defined in this work, which requires the subject classifier to possess cross-action generalization capabilities. To achieve that, we present a novel pose-based Hough forest framework, in which each per-frame pose feature casts a probabilistic vote to the Hough space. Pose distribution is estimated from training data and then used to compute the reliability of the vote to deal with the unseen poses in the test action sequence. Experimental results with various real datasets demonstrate that the proposed method provides effective person identification results especially for the challenging cross-action person identification setting.