Real-time multi-objective hand posture/gesture recognition by using distance classifiers and finite state machine for virtual mouse operations

Cameras that are connected to computers record sequence of digital images of human hand in order to interpret human posture/gesture. Human hand posture/gesture recognition has been utilized for providing virtual reality mechanism and it is still an ongoing research in human-computer interaction (HCI) community. Virtual reality can be operated on a particular program but it will be more effective if the entire system can be controlled for the sake of generality. Another direction is the applicability of virtual reality in real time. In this paper, we have developed a virtual mouse system that can recognize the pre-defined mouse movements in real time regardless of the context. Our real time hand recognition system is three fold. 1) skin detection, 2) feature extraction and 3) recognition. For recognition, various features with their own objectives are constructed from hand postures andcompared according to the similarity measures and the best-matched posture is used as a mouse action to control the cursor of the computer.

[1]  Thomas S. Huang,et al.  Constructing finite state machines for fast gesture recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[2]  Augustine Tsai,et al.  Hand posture recognition using Hidden Conditional Random Fields , 2009, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[3]  Thomas S. Huang,et al.  Gesture modeling and recognition using finite state machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[4]  Vijayan K. Asari,et al.  Hull convexity defects features for human activity recognition , 2010, 2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR).

[5]  Wen Gao,et al.  A Chinese sign language recognition system based on SOFM/SRN/HMM , 2004, Pattern Recognit..

[6]  Seong-Whan Lee,et al.  Recognizing hand gestures using dynamic Bayesian network , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[7]  Israr Ahmed,et al.  Pakistan Sign Language Recognition Using Statistical Template Matching , 2007 .

[8]  Cristina Manresa-Yee,et al.  Hand Tracking and Gesture Recognition for Human-Computer Interaction , 2009, Progress in Computer Vision and Image Analysis.

[9]  Jack Sklansky,et al.  Finding the convex hull of a simple polygon , 1982, Pattern Recognit. Lett..

[10]  Paul Modler,et al.  Recognition of separate hand gestures by Time-Delay Neural Networks based on multi-state spectral image patterns from cyclic hand movements , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[11]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[12]  Mika Laaksonen,et al.  Skin detection in video under changing illumination conditions , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  Helton Hideraldo Bíscaro,et al.  Hand movement recognition for Brazilian Sign Language: A study using distance-based neural networks , 2009, 2009 International Joint Conference on Neural Networks.

[14]  Vladimir Vezhnevets,et al.  A Survey on Pixel-Based Skin Color Detection Techniques , 2003 .

[15]  Raimondo Schettini,et al.  Skin segmentation using multiple thresholding , 2006, Electronic Imaging.

[16]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[17]  Carlos R. P. Dionisio,et al.  A Supervised Shape Classification Technique Invariant under Rotation and Scaling , 2002 .

[18]  Tarek M. Mahmoud.  A New Fast Skin Color Detection Technique , 2008 .

[19]  Balazs Tusor,et al.  Circular fuzzy neural network based hand gesture and posture modeling , 2010, 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings.

[20]  H.H. Aviles-Arriaga,et al.  Visual recognition of gestures using dynamic naive Bayesian classifiers , 2003, The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003..

[21]  Mariusz Flasinski,et al.  On the use of graph parsing for recognition of isolated hand postures of Polish Sign Language , 2010, Pattern Recognit..

[22]  Heung-Il Suk,et al.  Robust modeling and recognition of hand gestures with dynamic Bayesian network , 2008, 2008 19th International Conference on Pattern Recognition.

[23]  Heung-Il Suk,et al.  Hand gesture recognition based on dynamic Bayesian network framework , 2010, Pattern Recognit..