@inproceedings{KopinskiSacharaGepperthetal.2016,
  author    = {Thomas Kopinski and Fabian Sachara and Alexander Gepperth and Uwe Handmann},
  title     = {A Deep Learning Approach for Hand Posture Recognition From Depth Data},
  year      = {2016},
  abstract  = {Given the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN’s ability of automated feature generation and combine it with a novel 3D feature computation technique, preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5 \%.},
  language  = {en}
}