A Deep Learning Approach for Hand Posture Recognition from Depth Data

Thomas Kopinski (1) , Fabian Sachara (1) , Alexander Gepperth (2, 3) , Uwe Handmann (1)

1 HRW - Hochschule Ruhr West
2 Flowers - Flowing Epigenetic Robots and Systems
3 RV - Robotique et Vision

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%.

Mots clés: deep learning; hand posture recognition; 3D data

Domaines: Apprentissage [cs.LG]

Thomas Kopinski, Fabian Sachara, Alexander Gepperth, Uwe Handmann. A Deep Learning Approach for Hand Posture Recognition from Depth Data. International Conference on Artificial Neural Networks (ICANN), 2016, Barcelona, Spain. pp.179 - 186, ⟨10.1007/978-3-319-44781-0_22⟩. ⟨hal-01418137⟩