Taichi Murase
ABSTRACT
In this paper, the authors propose a novel gesture-based virtual keyboard (Gesture Keyboard) that uses a standard QWERTY keyboard layout, and requires only one camera, and employs a machine learning technique. Gesture Keyboard tracks the user's fingers and recognizes finger motions to judge keys input in the horizontal direction. Real-Adaboost (Adaptive Boosting), a machine learning technique, uses HOG (Histograms of Oriented Gradients) features in an image of the user's hands to estimate keys in the depth direction. Each virtual key follows a corresponding finger, so it is possible to input characters at the user's preferred hand position even if the user displaces his hands while inputting data. Additionally, because Gesture Keyboard requires only one camera, keyboard-less devices can implement this system easily. We show the effectiveness of utilizing a machine learning technique for estimating depth.
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- Frank Chun Yat Li, et al. The 1Line Keyboard: A QWERTY Layout in a Single Line. UIST '11. ACM, Santa Barbara, CA, USA Google ScholarDigital Library
- Juan Pablo Wachs, et al. Vision-Based Hand-Gesture Applications in communications of the ACM (February 2011, vol.54, No2) Google ScholarDigital Library
- Mathias Kolsch, et al. Keyboards without Keyboards: A Survey of Virtual Keyboards. UCSB technical Report 2002--21, July 12, 2002Google Scholar
- Taich Murase, et al. Gesture Keyboard Requiring Only One Camera. UIST '11. ACM, Santa Barbara, CA, USA Google ScholarDigital Library
- Vkey: Virtual Devices, Inc., http://www.virtualdevices.net/index2.htmGoogle Scholar
Index Terms
- Gesture keyboard with a machine learning requiring only one camera
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