International Journal of Electrical, Electronics and Data Communication (IJEEDC)
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Volume-7,Issue-12  ( Dec, 2019 )
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 84
Paper Published : 1397
No. of Authors : 3784
  Journal Paper

Paper Title
3-D Hand Pose Recognition from a Pair of Depth and Geodesic Images using Deep Convolutional Neural Network

Abstract
Accurate 3-D hand-pose recognition is one of thenovel user interface technologies that can facilitate interactions between humans and smart devices. In this work, we propose a methodology to recognize 3-D hand pose from a pair of depth and geodesic distance images of a hand. First, we train a Convolutional Neural Network (CNN) regressorwith a database of depth and Geodesic images of a hand along with the ground-truth joint positions.Second, using the trained CNN regressor, we estimate the 3-D joint positions from input pairs of depth and Geodesic images. Finally, based on the estimated joint positions, we reconstruct 3-D hand poses. Our results show that making use of Geodesic distancealongwith depthinformationimproves 3-D hand pose recognition by enhancing the capacity of regression via CNN Index Terms - Depth image, Geodesic image, 3-D hand pose recognition, Deep learning, Convolutional neural network


Author - J. M. Park, G. Gi, T. Y. Kim, H. M. Park, T.-S.Kim

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