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
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 f
eatures from various networks and discuss usefulness for various applications.
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