Browse > Article

A Study on Hand Gesture Recognition with Low-Resolution Hand Images  

Ahn, Jung-Ho (강남대학교 컴퓨터미디어정보공학부)
Publication Information
Journal of Satellite, Information and Communications / v.9, no.1, 2014 , pp. 57-64 More about this Journal
Abstract
Recently, many human-friendly communication methods have been studied for human-machine interface(HMI) without using any physical devices. One of them is the vision-based gesture recognition that this paper deals with. In this paper, we define some gestures for interaction with objects in a predefined virtual world, and propose an efficient method to recognize them. For preprocessing, we detect and track the both hands, and extract their silhouettes from the low-resolution hand images captured by a webcam. We modeled skin color by two Gaussian distributions in RGB color space and use blob-matching method to detect and track the hands. Applying the foodfill algorithm we extracted hand silhouettes and recognize the hand shapes of Thumb-Up, Palm and Cross by detecting and analyzing their modes. Then, with analyzing the context of hand movement, we recognized five predefined one-hand or both-hand gestures. Assuming that one main user shows up for accurate hand detection, the proposed gesture recognition method has been proved its efficiency and accuracy in many real-time demos.
Keywords
gesture recognition; hand detection and tracking; hand segmentation; silhouette mode analysis; skin color model; floodfill algorithm;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Fang, K. Wang, J. Cheng and H. Lu, "A real-time hand gesture recognition method", IEEE International Conference on Multimedia and Expo, pp. 995-998, 2007.
2 A. F. Bobick, and J. W. Davis, "The Recognition of Human Movement Using Temporal Templates", IEEE Transactions on Pattern Recognition and Machine Learning, Vol. 23, No. 3, 2001.
3 J.W. Davis, "Hierarchical Motion History Images for Recognizing Human Motion", IEEE workshop on Detection and Recognition of Events in Video, pp.39-46, 2001.
4 N. D. Binh, E. Shuichi and T. Ejima, "Real-Time Hand Tracking and Gesture Recognition System", GVIP Conference, 2005.
5 L. Bretzber, I. Laptev, Tony Lindeberg, "Hand Gesture Recognition using Mulit-Scale Colour Features, Hierarchical Models and Partical Filtering", IEEE Interntional Conference on Automatic Face and Gesture Recognition, pp.423-428. 2002.
6 H. Kim, G. Albuquerque, S. Havemann, D. W. Fellner, "Tangible 3D: Hand Gesture Interaction for Immersive 3D Modeling", IPT & EGVE Workshop, 2005.
7 Microsoft Project Natal, http://research.microsoft .com/apps/video/default.aspx?id=144455 .
8 P. Viola and M. Jones, "Robust Real-time Face Detection", International Journal of Computer Vision, vol.57, No. 2, pp. 137-154, 2001.
9 T. Kirishima, K. Sato, K., Chihara, "Real-Time Gesture Recognition by Learning and Selective Control of Visual Interest Points", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 3, pp.351-364, 2005.   DOI
10 A. Malima, E. Ozgur, M. Cetin, "A Fast Algorithm for Vision-based Hand Gesture Recognition for Robot Control", IEEE Conference on Signal Processing and Communications Applications, 2006.
11 H.-S. Yoon, J. Soh, Y. J. Bae, H. S. Yang, "Hand gesture recognition using combined features of location, angle and velocity", Pattern Recognition, Vol. 34, pp. 1491-1501, 2001.   DOI
12 X. Xiong and F. D. Torre, "Supervised Descent Method and its Applications to Face Alignment", IEEE Conference on Computer Vision and Pattern Recognition, 2013.
13 J.-H. Ahn and J.-H. Kim, "A Stable Hand Tracking Method by Skin Color Blob Matching", Pacific Science Review, Vol.12, No.2, pp. 146-151, 2010.