Gesture Recognition Method Combining Dense Convolutional with Spatial Transformer Networks
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摘要: 手势识别作为人机交互的方式之一,在人工智能日益发展的今天备受瞩目。针对手势旋转、平移、缩放等形变导致识别率偏低的问题,该文基于密集卷积网络(Densenet)与空间转换网络(STN)提出了一种新型的网络结构Densenet_V2,先利用空间转换网络对输入的样本和特征图进行空间变换和对齐,再利用密集卷积网络自动提取手势的有效特征,最后通过线性分类器对手势进行分类。为防止网络模型对样本数据集过度拟合,对网络进行训练时在损失函数中加入L2正则项以实现权重衰减。在Marcel手势库上进行多次实验。实验结果表明,Densenet_V2可以提高对静态形变手势的识别率。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.
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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. 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. 阮晓钢, 林佳, 于乃功, 等. 基于多线索的运动手部分割方法[J]. 电子与信息学报, 2017, 39(5): 1088-1095. doi: 10.11999/ JEIT160730. 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. 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. 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. 杨学文, 冯志全, 黄忠柱, 等. 结合手势主方向和类- Hausdorff距离的手势识别[J]. 计算机辅助设计与图形学学报, 2016, 28(1): 75-81. doi: 10.3969/j.issn.1003-9775.2016.01.010. 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. 刘淑萍, 刘羽, 於俊, 等. 结合手指检测和HOG特征的分层静态手势识别[J]. 中国图象图形学报, 2015, 20(6): 781-788. doi: 10.11834/jig.20150607. 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. 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. 杜堃, 谭台哲. 复杂环境下通用的手势识别方法[J]. 计算机应用, 2016, 36(7): 1965-1970. doi: 10.11772/j.issn.1001-9081. 2016.07.1965. 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. 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. 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. JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[OL]. https://arxiv.org/abs/ 1506.02025v3,2015. LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539. GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Massachusetts, USA: MIT Press, 2016: 231-234.
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