Vision-based hand-gesture applications
Communications of the ACM
Gesture keyboard requiring only one camera
Proceedings of the 24th annual ACM symposium adjunct on User interface software and technology
The 1line keyboard: a QWERTY layout in a single line
Proceedings of the 24th annual ACM symposium on User interface software and technology
Real time mono-vision based customizable virtual keyboard using finger tip speed analysis
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
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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.