Abstract:As an important milestone for the development of the artificial intelligence, gesture recognition enables the human-computer interaction and has received significantly growing research interest nowadays. However, the current technology for the gesture recognition has the low quality in the gesture rotation, translation and scaling. To solve the problem, a novel network structure named Densenet_V2 is proposed, and it is based on Dense Convolutional Networks (Densenet) and Spatial Transformer Networks (STN). Firstly, the input samples and feature maps are spatially transformed and aligned with the STN. Then the effective features of gestures are automatically extracted by using the Densenet. Finally, the linear classifier is adopted to classify the gestures. To prevent the network model from over-fitting the sample data set, the L2 regular term is involved into the loss function to achieve the weight decay when training the network. Experiments on the Marcel gesture database show that Densenet_V2 can improve the recognition rate of static deformation gestures.
PIYUSH K, SIDDHARTH S R, and Anupam A. Hand data glove: A new generation real-time mouse for human- computer interaction[C]. International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, Jharkand, India, 2012: 750-755. doi: 10.1109/RAIT.2012. 6194548.
[2]
WEI W and JING P. Hand segmentation using skin color and background information[C]. International Conference on Machine Learning and Cybernetics, Xi,an, China, 2012: 1487-1492. doi: 10.1109/ICMLC.2012.6359584.
RUAN Xiaogang, LIN Jia, YU Naigong, et al. Moving hand segmentation based on multi-cues[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1088-1095. doi: 10. 11999/JEIT160730.
[4]
LIU Y, YIN Y, and ZHANG S. Hand gesture recognition based on HU moments in interaction of virtual reality[C]. International Conference on Intelligent Human-Machine Systems and Cybernetics, Nanchang, China, 2012: 145-148. doi: 10.1109/IHMSC.2012.42.
[5]
DARDAS N H and GEORGANAS N D. Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques[J]. IEEE Transactions on Instrumentation & Measurement, 2011, 60(11): 3592-3607. doi: 10.1109/TIM.2011.2161140.
YANG Xuewen, FENG Zhiquan, HUANG Zhongzhu, et al. Gesture recognition based on combining main direction of gesture and Hausdorff-like distance[J]. Journal of Computer- Aided Design & Computer Graphics, 2016, 28(1): 75-81. doi: 10.3969/j.issn.1003-9775.2016.01.010.
LIU Shuping, LIU Yu, YU Jun, et al. Hierarchical static hand gesture recognition by combining finger detection and HOG features[J]. Journal of Image and Graphics, 2015, 20(6): 781-788. doi: 10.11834/jig.20150607.
[8]
LIN H I, HSU M H, and CHEN W K. Human hand gesture recognition using a convolution neural network[C]. IEEE International Conference on Automation Science and Engineering, Taipei, China, 2014: 1038-1043. doi: 10.1109/ CoASE.2014.6899454.
DU Kun and TAN Taizhe. General method for gesture recognition in complex environment[J]. Journal of Computer Applications, 2016, 36(7): 1965-1970. doi: 10.11772/j.issn. 1001-9081.2016.07.1965.
[10]
PYO J, JI S, and YOU S. Depth-based hand gesture recognition using convolutional neural networks[C]. International Conference on Ubiquitous Robots and Ambient Intelligence, Xi,an, China, 2016: 225-227. doi: 10.1109/URAI. 2016.7625742.
[11]
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791.
[12]
SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[OL]. http://arxiv. org/abs/1409.1556, 2014.
[13]
HUANG G, LIU Z, WEINBERGER K Q, et al. Densely connected convolutional networks[OL]. http://arxiv.org/abs/ 1608.06993, 2016.
[14]
JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[OL]. https://arxiv.org/abs/ 1506.02025v3,2015.
[15]
LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539.
[16]
GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Massachusetts, USA: MIT Press, 2016: 231-234.