Towards The Development of Virtual Keyboard: An Activity Recognition Approach

This paper presents an activity recognition based scheme for the development of the virtual keyboard for mobile and portable computing devices. Keystrokes activities are modeled in terms of the joints movements of fingers using kinematic model of hand. Generic activity recognition system is employed for keystroke detection and recognition. Hands activities on a flat surface are captured using mobile's secondary camera. Scheme composed of two steps: first, low-level features are extracted in terms of joints trajectories estimation using finger joints localization and optical flow calculation. Second, trajectories are further interpreted into feature vectors. Feature vectors are trained and classified using Hidden Markov Models leading towards the keystrokes recognition. Real- time implementation covers the accurate detection and recognition of 78 keys of the keyboard, thus providing enriched set of keys to the users of mobile devices. (Yousaf MH, Habib HA, Azhar K, Hussain F, Rizwan M, Asim MM. Towards the Development of Virtual Keyboard: An Activity Recognition Approach. Life Sci J 2013;10(10s):275-282) (ISSN:1097-8135). http://www.lifesciencesite.com. 45

[1]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Woontack Woo,et al.  ARKB: 3D vision-based Augmented Reality Keyboard , 2003, ICAT.

[3]  T. Saba,et al.  Off-line cursive script recognition: current advances, comparisons and remaining problems , 2012, Artificial Intelligence Review.

[4]  Rehman,et al.  Video motion perception using optimized Gabor filter , 2011 .

[5]  Amjad Rehman,et al.  Performance analysis of character segmentation approach for cursive script recognition on benchmark database , 2011, Digit. Signal Process..

[6]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[7]  Yang Liu,et al.  A robust hand tracking and gesture recognition method for wearable visual interfaces and its applications , 2004, Third International Conference on Image and Graphics (ICIG'04).

[8]  Luca Lombardi,et al.  Adding Gestures to Ordinary Mouse Use: a New Input Modality for Improved Human-Computer Interaction , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[9]  Robert Rosenberg.  Computing without Mice and Keyboards: Text and Graphic Input Devices for Mobile Computing , 1998 .

[10]  E. Charbon,et al.  A virtual keyboard system based on Multi-Level Feature Matching , 2008, 2008 Conference on Human System Interactions.

[11]  Ghazali Sulong,et al.  Dynamic Programming Based Hybrid Strategy for Offline Cursive Script Recognition , 2010, 2010 Second International Conference on Computer Engineering and Applications.

[12]  Ming Xie,et al.  Finger identification and hand posture recognition for human-robot interaction , 2007, Image Vis. Comput..

[13]  Yoshinobu Tonomura,et al.  “Body coupled FingerRing”: wireless wearable keyboard , 1997, CHI.

[14]  Amjad Rehman,et al.  Neural networks for document image preprocessing: state of the art , 2014, Artificial Intelligence Review.

[15]  Chun-Jen Chen,et al.  A linear-time component-labeling algorithm using contour tracing technique , 2004, Comput. Vis. Image Underst..

[16]  Amjad Rehman,et al.  Methods and strategies on off-line cursive touched characters segmentation: a directional review , 2014, Artificial Intelligence Review.

[17]  Ying Wu,et al.  Visual panel: virtual mouse, keyboard and 3D controller with an ordinary piece of paper , 2001, PUI '01.

[18]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[19]  Amjad Rehman,et al.  Effects of artificially intelligent tools on pattern recognition , 2013, Int. J. Mach. Learn. Cybern..

[20]  Amjad Rehman,et al.  Evaluation of Adaptive Subdivision Method on Mobile Device , 2013 .

[21]  Ghazali Sulong,et al.  An intelligent approach to image denoising , 2010 .

[22]  Amjad Rehman,et al.  Evaluation of artificial intelligent techniques to secure information in enterprises , 2012, Artificial Intelligence Review.

[23]  Peter H. N. de With,et al.  Automatic video-based human motion analyzer for consumer surveillance system , 2009, IEEE Transactions on Consumer Electronics.

[24]  Amjad Rehman,et al.  Virtual machine security challenges: case studies , 2014, Int. J. Mach. Learn. Cybern..

[25]  Tanzila Saba,et al.  ANALYSIS OF VISION BASED SYSTEMS TO DETECT REAL TIME GOAL EVENTS IN SOCCER VIDEOS , 2013, Appl. Artif. Intell..

[26]  Mircea Nicolescu,et al.  Vision-based hand pose estimation: A review , 2007, Comput. Vis. Image Underst..

[27]  David G. Stork,et al.  Pattern Classification , 1973 .

[28]  Kang Ryoung Park,et al.  A realistic game system using multi-modal user interfaces , 2010, IEEE Transactions on Consumer Electronics.

[29]  Alexandra Branzan Albu,et al.  The visual keyboard: Real-time feet tracking for the control of musical meta-instruments , 2008, Signal Process. Image Commun..

[30]  Edoardo Charbon,et al.  A Virtual Keyboard Based on True-3D Optical Ranging , 2005, BMVC.

[31]  Amjad Rehman,et al.  An automatic approach for line detection and removal without smash-up characters , 2011 .

[32]  Hafiz Adnan Habib,et al.  Real time mono vision gesture based virtual keyboard system , 2006, IEEE Transactions on Consumer Electronics.