Image Processing and Recognition of Multiple Static Hand Gestures for Human-Computer Interaction

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). However, the number of hand gestures has not been fully explored for HCI application. It is necessary to achieve more gestures as the command of interface. This paper proposed a method to recognize nine different hand gestures. Using camera to get images of people wearing pink gloves, and then preprocess those images by color splitting, morphological processing and edge extraction. Fourier descriptor, edge histogram and boundary moment invariants are three methods of feature extraction. At last, the template matching was used to realize the hand gesture recognition. The average recognition rate of the nine different gestures employing three different methods is 0.859.

[1]  Rogério Schmidt Feris,et al.  The isometric self-organizing map for 3D hand pose estimation , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[2]  Lalit Gupta,et al.  Gesture-based interaction and communication: automated classification of hand gesture contours , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[3]  Yen-Wei Chen,et al.  Articulated hand tracking by PCA-ICA approach , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[4]  Thomas S. Huang,et al.  Tracking articulated hand motion with eigen dynamics analysis , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[5]  James M. Rehg,et al.  Singularity analysis for articulated object tracking , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  Zhang Li-cai.  Static Sign Language Recognition Based on Edge Gradient Direction Histogram , 2010 .

[7]  David Zeltzer,et al.  A survey of glove-based input , 1994, IEEE Computer Graphics and Applications.

[8]  Akira Iwata,et al.  A rotation invariant approach on static-gesture recognition using boundary histograms and neural networks , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..