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

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

Article Citation :J. M. Park ,G. Gi ,T. Y. Kim ,H. M. Park ,T.-S.Kim , (2017 ) " 3-D Hand Pose Recognition from a Pair of Depth and Geodesic Images using Deep Convolutional Neural Network " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 20-23, Volume-5,Issue-12

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

Type : Research paper

Published : Volume-5,Issue-12

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