A Collaborative Filtering Approach to Real-Time Hand Pose Estimation

by | Aug 31, 2015

Authors: Chiho Choi, Ayan Sinha, Joon Hee Choi, Sujin Jang, and Karthik Ramani
In Proc. IEEE International Conference on Computer Vision (ICCV) 2015, Santiago, Chile.


Collaborative filtering aims to predict unknown user ratings in a recommender system by collectively assessing known user preferences. In this paper, we first draw analogies between collaborative filtering and the pose estimation problem. Specifically, we recast the hand pose estimation problem as the cold-start problem for a new user with unknown item ratings in a recommender system. Inspired by fast and accurate matrix factorization techniques for collaborative filtering, we develop a real-time algorithm for estimating the hand pose from RGB-D data of a commercial depth camera. First, we efficiently identify nearest neighbors using local shape descriptors in the RGB-D domain from a library of hand poses with known pose parameter values. We then use this information to evaluate the unknown pose parameters using a joint matrix factorization and completion (JMFC) approach. Our quantitative and qualitative results suggest that our approach is robust to variation in hand configurations while achieving real time performance (29 FPS) on a standard computer.

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Chiho Choi

Chiho Choi

Chiho Choi is currently working at Honda Research Laboratory, Palo Alto, CA. He received his degree of Ph.D. in the school of Electrical and Computer Engineering at Purdue University. Dr. Choi received a B.S. from Hanyang University, Korea in 2011, and a M.S. from University of Southern California, in 2013. His research interest lies at the intersection of machine learning and computer vision. He broadly builds machine learning algorithms in a practical way for computer vision systems. Currently,Choi is working as a research assistant in the Computational Design and Innovation Lab led by Professor Karthik Ramani, focusing on human shape interaction. [Personal Website][LinkedIn]