Open AccessHGR‐Net: a fusion network for hand gesture segmentation and recognition
Author(s) -
Dadashzadeh Amirhossein,
Targhi Alireza Tavakoli,
Tahmasbi Maryam,
Mirmehdi Majid
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5796
Subject(s) - computer science , artificial intelligence , segmentation , pattern recognition (psychology) , convolutional neural network , pooling , pyramid (geometry) , gesture , gesture recognition , residual , deep learning , computer vision , image segmentation , network architecture , mathematics , geometry , computer security , algorithm
We propose a two‐stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR‐Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub‐network is trained without depth information, it is particularly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two‐stream CNN, which fuses the information from the red–green–blue and segmented images by combining their deep representations in a fully connected layer before classification. Extensive experiments on public datasets show that our architecture achieves almost as good as state‐of‐the‐art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size. Our method can operate at an average of 23 ms per frame.