TY - GEN
T1 - Fusion of deep learning descriptors for gesture recognition
AU - Escobedo Cardenas, Edwin
AU - Camara-Chavez, Guillermo
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - In this paper, we propose an approach for dynamic hand gesture recognition, which exploits depth and skeleton joint data captured by Kinect™ sensor. Also, we select the most relevant points in the hand trajectory with our proposed method to extract keyframes, reducing the processing time in a video. In addition, this approach combines pose and motion information of a dynamic hand gesture, taking advantage of the transfer learning property of CNNs. First, we use the optical flow method to generate a flow image for each keyframe, next we extract the pose and motion information using two pre-trained CNNs: a CNN-flow for flow-images and a CNN-pose for depth-images. Finally, we analyze different schemes to fusion both informations in order to achieve the best method. The proposed approach was evaluated in different datasets, achieving promising results compared to other methods, outperforming state-of-the-art methods.
AB - In this paper, we propose an approach for dynamic hand gesture recognition, which exploits depth and skeleton joint data captured by Kinect™ sensor. Also, we select the most relevant points in the hand trajectory with our proposed method to extract keyframes, reducing the processing time in a video. In addition, this approach combines pose and motion information of a dynamic hand gesture, taking advantage of the transfer learning property of CNNs. First, we use the optical flow method to generate a flow image for each keyframe, next we extract the pose and motion information using two pre-trained CNNs: a CNN-flow for flow-images and a CNN-pose for depth-images. Finally, we analyze different schemes to fusion both informations in order to achieve the best method. The proposed approach was evaluated in different datasets, achieving promising results compared to other methods, outperforming state-of-the-art methods.
KW - Convolutional neuronal networks
KW - Fusion methods
KW - Hand gesture recognition
KW - Keyframe extraction
KW - Pose and motion information
UR - https://www.scopus.com/pages/publications/85042214567
U2 - 10.1007/978-3-319-75193-1_26
DO - 10.1007/978-3-319-75193-1_26
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85042214567
SN - 9783319751924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 212
EP - 219
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
A2 - Velastin, Sergio
A2 - Mendoza, Marcelo
PB - Springer Verlag
T2 - 22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
Y2 - 7 November 2017 through 10 November 2017
ER -