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Hand Posture Recognition Using Convolutional Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

In this work we present a convolutional neural network-based algorithm for recognition of hand postures on images acquired by a single color camera. The hand is extracted in advance on the basis of skin color distribution. A neural network-based regressor is applied to locate the wrist. Finally, a convolutional neural network trained on 6000 manually labeled images representing ten classes is executed to recognize the hand posture in a sub-window determined on the basis of the wrist. We show that our model achieves high classification accuracy, including scenarios with different camera used in testing. We show that the convolutional network achieves better results on images pre-filtered by a Gabor filter.

Keywords

Gesture recognition Biologically inspired computer vision Gabor filter Convolutional neural network 

Notes

Acknowledgment

This work was supported by Polish National Science Center (NCN) under a research grant 2014/15/B/ST6/02808.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.National University of EngineeringLimaPeru

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