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A time-of-flight-based hand posture database for human-machine interaction

  • We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.

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Author:Thomas Kopinski, Alexander Gepperth, Uwe Handmann
Document Type:Conference Proceeding
Year of Completion:2016
Release Date:2019/07/04
Institutes:Fachbereich 1 - Institut Informatik
DDC class:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft
Licence (German):License LogoNo Creative Commons