[1]WEI Zhixin,FANG Yongchun.Autonomous developmental network incorporating human cognitive modes and its application in gesture recognition[J].CAAI Transactions on Intelligent Systems,2023,18(1):144-152.[doi:10.11992/tis.202212002]
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Autonomous developmental network incorporating human cognitive modes and its application in gesture recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 1 Page number: 144-152 Column: 人工智能院长论坛 Public date: 2023-01-05
Title:
Autonomous developmental network incorporating human cognitive modes and its application in gesture recognition
Author(s):
WEI Zhixin1;  FANG Yongchun1; 2
1. Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300071, China;
2. College of Artifical Intelligence, Nankai University, Tianjin 300071, China
Keywords:
autonomous developmental network; gesture recognition; human cognitive modes; top-k competition mechanism; neuron cognitive ability feedback; synaptic readjustment; incremental hierarchical discriminant regression; lobe component analysis
CLC:
TP183
DOI:
10.11992/tis.202212002
Abstract:
The autonomous development algorithm has broad application prospect in the intelligent robots and other fields. Considering the limitation of existing methods, this paper proposes an autonomous development neural network integrating human cognitive mode, and applies the method to gesture recognition tasks. Specifically, the proposed method dynamically changes the human-guided part for the computation of neuron pre-response values to simulate the way humans learn. In addition, we propose a top-k competition mechanism based on the dynamic k value. Besides, in the method, human brain’s ability of receiving and memorizing knowledge is simulated to update the synaptic weights of the winning neurons, and neuron cognitive ability is utilized as the feedback for synaptic readjustment. Comparative experimental results show that, compared with the original method, the improved autonomic development network in this paper has better learning effect and higher recognition rate in gesture recognition task.
References:
[1] FENG Zhao, WU Jinlong, NI Taile. Research and application of multifeature gesture recognition in human-computer interaction based on virtual reality technology[J]. Wireless communications and mobile computing, 2021, 10: 1–5.
[2] LI Jiayao, RAY S, RAJANNA V, et al. Evaluating the performance of machine learning algorithms in gaze gesture recognition systems[J]. IEEE access, 2021, 10: 1020–1035.
[3] HUANG Dengyuan, HU W C, CHANG S H. Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination[J]. Expert systems with applications, 2011, 38(5): 6031–6042.
[4] TARVEKAR M P. Hand gesture recognition system for touch-less car interface using multiclass support vector machine[C]//2018 Second International Conference on Intelligent Computing and Control Systems. Madurai: IEEE, 2019: 1929?1932.
[5] RAHIM M A, MIAH A S M, SAYEED A, et al. Hand gesture recognition based on optimal segmentation in human-computer interaction[C]//2020 3rd IEEE International Conference on Knowledge Innovation and Invention. Kaohsiung: IEEE, 2021: 163?166.
[6] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the acm, 2017, 60(6): 84–90.
[7] GUO Xing, XU Wu, TANG Wen quan, et al. Research on optimization of static gesture recognition based on convolution neural network[C]//2019 4th International Conference on Mechanical, Control and Computer Engineering. Hohhot: IEEE, 2020: 398?3982.
[8] SIMONYAN K, ZISSERMAN A. Two-stream convolutional networks for action recognition in videos[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems?Volume 1. Montreal: ACM, 2014: 568?576.
[9] TRAN D, BOURDEV L, FERGUS R, et al. Learning spatiotemporal features with 3D convolutional networks[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2016: 4489?4497.
[10] MOLCHANOV P, GUPTA S, KIM K, et al. Multi-sensor system for driver’s hand-gesture recognition[C]//2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. Ljubljana: IEEE, 2015: 1?8.
[11] OYEDOTUN O K, KHASHMAN A. Deep learning in vision-based static hand gesture recognition[J]. Neural computing and applications, 2017, 28(12): 3941–3951.
[12] ELBADAWY M, ELONS A S, SHEDEED H A, et al. Arabic sign language recognition with 3D convolutional neural networks[C]//2017 Eighth International Conference on Intelligent Computing and Information Systems. Cairo: IEEE, 2018: 66?71.
[13] CARREIRA J, ZISSERMAN A. Quo vadis, action recognition? A new model and the kinetics dataset[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4724?4733.
[14] FANG Wei, DING Yewen, ZHANG Feihong, et al. Gesture recognition based on CNN and DCGAN for calculation and text output[J]. IEEE access, 2019, 7: 28230–28237.
[15] KOLLER O, NEY H, BOWDEN R. Deep hand: how to train a CNN on 1 million hand images when your data is continuous and weakly labelled[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 3793?3802.
[16] JAHANDAD, SAM S M, KAMARDIN K, et al. Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3[J]. Procedia computer science, 2019, 161: 475–483.
[17] WENG Juyang, LUCIW M. Dually optimal neuronal layers: lobe component analysis[J]. IEEE transactions on autonomous mental development, 2009, 1(1): 68–85.
[18] WENG Juyang, MCCLELLAND J, PENTLAND A, et al. Autonomous mental development by robots and animals[J]. Science, 2001, 291(5504): 599–600.
[19] WENG Juyang, HWANG W S. Incremental hierarchical discriminant regression[J]. IEEE transactions on neural networks, 2007, 18(2): 397–415.
[20] 黄敏, 路飞, 李晓磊, 等. 基于IHDR算法和BP神经网络复合框架的机器人服务自主认知和发育系统[J]. 机器人, 2019, 41(5): 609–619
HUANG Min, LU Fei, LI Xiaolei, et al. Autonomous cognition and development system of robot service based on a composite framework combining IHDR algorithm with BP neural network[J]. Robot, 2019, 41(5): 609–619
[21] LU Fei, HUANG Min, LI Xiaolei, et al. Learning and development of home service robots’ service cognition based on a learning mechanism[J]. Applied sciences, 2020, 10(2): 464.
[22] 钱夔, 宋爱国, 章华涛, 等. 基于自主发育神经网络的机器人室内场景识别[J]. 机器人, 2013, 35(6): 703–708,743
QIAN Kui, SONG Aiguo, ZHANG Huatao, et al. Robot indoor scenes recognition based on autonomous developmental neural network[J]. Robot, 2013, 35(6): 703–708,743
[23] 王东署, 谭达佩, 韦晓琴. 基于发育网络的人脸朝向识别研究[J]. 郑州大学学报(工学版), 2017, 38(5): 23–27
WANG Dongshu, TAN Dapei, WEI Xiaoqin. Study of face orientation recognition based on development network[J]. Journal of Zhengzhou university (engineering science edition), 2017, 38(5): 23–27
[24] CASTRO-GARCIA J L, WENG Juyang. Emergent multilingual language acquisition using developmental networks[C]//2019 International Joint Conference on Neural Networks. Budapest: IEEE, 2019: 1?8.
[25] ZHENG Zejia, WU Xiang, WENG Juyang. Emergent neural Turing machine and its visual navigation[J]. Neural networks, 2019, 110: 116–130.
[26] 余慧瑾, 方勇纯. 基于改进型自主发育网络的机器人场景识别方法[J]. 自动化学报, 2021, 47(7): 1530–1538
YU Huijin, FANG Yongchun. A robot scene recognition method based on improved autonomous developmental network[J]. Acta automatica sinica, 2021, 47(7): 1530–1538
[27] 余慧瑾, 方勇纯, 韦知辛. 基于多传感融合的自主发育网络场景识别方法[J]. 机器人, 2021, 43(6): 706–714
YU Huijin, FANG Yongchun, WEI Zhixin. A scene recognition method of autonomous developmental network based on multi-sensor fusion[J]. Robot, 2021, 43(6): 706–714
[28] 崔丽, 曾凤章. 基于“学习曲线”效应的长期质量成本模型[J]. 商业研究, 2004(24): 46–48
CUI Li, ZENG Fengzhang. A model of long-term quality cost based on learning curve effect[J]. Commercial research, 2004(24): 46–48
[29] MEMO A, MINTO L, ZANUTTIGH P. Exploiting silhouette descriptors and synthetic data for hand gesture recognition[C]//Smart Tools and Applications in Graphics - Italian Chapter Conference 2015. Verona: The Eurographics Association, 2015.
[30] MEMO A, ZANUTTIGH P. Head-mounted gesture controlled interface for human-computer interaction[J]. Multimedia tools and applications, 2018, 77(1): 27–53.
[31] BAKHEET S, AL-HAMADI A. Hand gesture recognition using optimized local Gabor features[J]. Journal of computational and theoretical nanoscience, 2017, 14(3): 1380–1389.
[32] BAKHEET S, AL-HAMADI A. Robust hand gesture recognition using multiple shape-oriented visual cues[J]. EURASIP journal on image and video processing, 2021, 2021(1): 26.
[33] TASMERE D, AHMED B, DAS S R. Real time hand gesture recognition in depth image using CNN[J]. International journal of computer applications, 2021, 174(16): 28–32.
[34] SAHANA T, BASU S, NASIPURI M, et al. MRCS: multi-radii circular signature based feature descriptor for hand gesture recognition[J]. Multimedia tools and applications, 2022, 81(6): 8539–8560.
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