Browse > Article

Dynamic Hand Gesture Recognition Using CNN Model and FMM Neural Networks  

Kim, Ho-Joon (School of Computer Science and Eletric Engineering, Handong University)
Publication Information
Journal of Intelligence and Information Systems / v.16, no.2, 2010 , pp. 95-108 More about this Journal
Abstract
In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.
Keywords
Hand Gesture Recognition; Neural Network; Feature Extraction; Pattern Classification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back : Face Recognition : A Convolutional Neural Network Approach, IEEE Transactions on Neural Networks, Vol.8, No.1(1997), 98-113.   DOI   ScienceOn
2 Ronald Poppe, "Vision-Based Human Motion Analysis: An overview", Computer Vision and Image Understanding, Vol.108, No.1(2007), 4-18.   DOI   ScienceOn
3 Cristophe Garcia, Manolis Delakis : Convolutional Face Finder : A Neural Architecture for Fast and Robust Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.11(2004), 1408-1423.   DOI   ScienceOn
4 P. K. Simpson, "Fuzzy Min-Max Neural Networks-Part 2 : Clustering", IEEE Transactions on Fuzzy Systems, Vol.1, No.1(1993), 32-45.   DOI
5 Markus Vincze et al., "Integrated Vision System for the Semantic Interpretation of Activities Where a Person Handles Objects", Computer Vision and Image Understanding, Vol.113, No.1(2009), 582-692.
6 Xiaofei Ji and Honghai, "Advances in View-Invariant Human Motion Analysis : A Review", IEEE Transaction on Systems, Man and Cybernetics, Part C, Vol.40, No.1(2010), 13-14.
7 P. K. Simpson, "Fuzzy Min-Max Neural Networks-Part 1 : Classification", IEEE Transactions on Neural Networks, Vol.3, No.5(1992), 776-786.   DOI   ScienceOn
8 Bogdan Gabrys, Andrzej Bargiela, "General Fuzzy Min-Max Neural Network for Clustering and Classification", IEEE Transactions on Neural Networks, Vol.11, No.3(2000), 769-783.   DOI
9 Ho-Joon Kim, Juho Lee and Hyun-Seung Yang, "Robust Realtime Face Detectgion using Hybrid Neural Networks", Proceeding of 2006 International Comference on Intelligent Computing(ICIC2006), Vol.1(2006), 721-730.
10 이조셉, 박진희, 김호준, "동적 수신호 인식을 위한 복합형 신경망 모델", 2007 한국컴퓨터종합학술대회논문집, 1권(2007), 287-292.
11 Daniel Weinland, Remi Ronfard, Edmond Boyer, "Free Viewpoint Action Recognition using Motion History Volumes", Computer Vision and Image Understanding, Vol.104, No.1(2006), 249-257.   DOI
12 Alper Yilmaz, Mubarak Shah, "Actions Sketch : A Novel Action Representation", Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1(2005), 984-989.