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Authors: Aditya Tewari 1 ; Frederic Grandidier 2 ; Bertram Taetz 3 and Didier Stricker 3

Affiliations: 1 German Research Centre for Artificial Intelligence(DFKI) and IEE S.A., Germany ; 2 IEE S.A., Luxembourg ; 3 German Research Centre for Artificial Intelligence(DFKI), Germany

ISBN: 978-989-758-173-1

Keyword(s): CNN , Hand-Pose, Feature Transfer, Transfer Learning, Fine Tuning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Classification ; Computational Intelligence ; Feature Selection and Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: A new dataset for hand-pose is introduced. The dataset includes the top view images of the palm by Time of Flight (ToF) camera. It is recorded in an experimental setting with twelve participants for six hand-poses. An evaluation on the dataset is carried out with a dedicated Convolutional Neural Network (CNN) architecture for Hand Pose Recognition (HPR). This architecture uses a model-layer. The small size model layer creates a funnel shape network which adds a priori knowledge and constrains the network by modelling the degree of freedom of the palm, such that it learns palm features. It is demonstrated that this network performs better than a similar network without the prior added. A two-phase learning scheme which allows training the model on full dataset even when the classification problem is confined to a subset of the classes is described. The best model performs at an accuracy of 92%. Finally, we show the feature transfer capability of the network and compare the extracted fe atures from various networks and discuss usefulness for various applications. (More)

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Paper citation in several formats:
Tewari, A.; Grandidier, F.; Taetz, B. and Stricker, D. (2016). Adding Model Constraints to CNN for Top View Hand Pose Recognition in Range Images.In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 170-177. DOI: 10.5220/0005660301700177

@conference{icpram16,
author={Aditya Tewari. and Frederic Grandidier. and Bertram Taetz. and Didier Stricker.},
title={Adding Model Constraints to CNN for Top View Hand Pose Recognition in Range Images},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={170-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005660301700177},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Adding Model Constraints to CNN for Top View Hand Pose Recognition in Range Images
SN - 978-989-758-173-1
AU - Tewari, A.
AU - Grandidier, F.
AU - Taetz, B.
AU - Stricker, D.
PY - 2016
SP - 170
EP - 177
DO - 10.5220/0005660301700177

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