Application of convolutional neural networks for static hand gestures recognition under different invariant features

The present work proposes to recognize the static hand gestures taken under invariations features as scale, rotation, translation, illumination, noise and background. We use the alphabet of sign language of Peru (LSP). For this purpose, digital image processing techniques are used to eliminate or reduce noise, to improve the contrast under a variant illumination, to separate the hand from the background of the image and finally detect and cut the region containing the hand gesture. We use of convolutional neural networks (CNN) to classify the 24 hand gestures. Two CNN architectures were developed with different amounts of layers and parameters per layer. The tests showed that the first CNN has an accuracy of 95.37% and the second CNN has an accuracy of 96.20% in terms of recognition of the 24 static hand gestures using the database developed. We compared the two architectures developed in accuracy level for each type of invariance presented in this paper. We compared the two architectures developed and usual techniques of machine learning in results of accuracy.

[1]  Zhang Peng,et al.  An Automatic Hand Gesture Recognition System Based on Viola-Jones Method and SVMs , 2009, 2009 Second International Workshop on Computer Science and Engineering.

[2]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[3]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[4]  Thao Nguyen Thi Huong,et al.  Static hand gesture recognition for vietnamese sign language (VSL) using principle components analysis , 2015, 2015 International Conference on Communications, Management and Telecommunications (ComManTel).

[5]  D. Ghosh,et al.  Trajectory modeling in gesture recognition using CyberGloves/sup /spl reg// and magnetic trackers , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[6]  P. Ganesan,et al.  International Conference on Recent Trends in Computing 2015 ( ICRTC-2015 ) Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space , 2015 .

[7]  Jovan Popović,et al.  Real-time hand-tracking with a color glove , 2009, SIGGRAPH 2009.

[8]  Elsayed A. Sallam,et al.  Hand gesture recognition using fourier descriptors , 2013, 2013 8th International Conference on Computer Engineering & Systems (ICCES).

[9]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[10]  Cyrus Shahabi,et al.  An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics , 2007, Int. J. Bioinform. Res. Appl..

[11]  Hong Yan,et al.  Sign Language Finger Alphabet Recognition from Gabor-PCA Representation of Hand Gestures , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[12]  Youdong Ding,et al.  Recognition of hand-gestures using improved local binary pattern , 2011, 2011 International Conference on Multimedia Technology.

[13]  Dimitrios Hatzinakos,et al.  Static hand gesture recognition using discriminative 2D Zernike moments , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.

[14]  S. Veluchamy,et al.  Hand gesture recognition system for real-time application , 2014, 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies.

[15]  Fang Yuan,et al.  Static hand gesture recognition based on HOG characters and support vector machines , 2013, 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA).