In this paper we propose a proprietary static hand pose database called OUHANDS and protocols for training and evaluating hand pose classification and hand detection methods. A comparison between the OUHANDS database and existing databases is given. Baseline results for both of the protocols are presented.

No abstract

We introduce a novel user interface solution for mobile devices

This paper introduces a new face tracking approach for controlling user interfaces in hand-held mobile devices. The proposed method detects the face and the eyes of the user by employing a method based on local texture features and boosting. An extended Kalman filter combines local motion features extracted from the face region and the detected eye positions to estimate the 3-D position and orientation of the camera with respect to the face. The camera position is used as an input for the spatially aware user interface. Experimental results on real image sequences captured with a camera-equipped mobile phone validate the feasibility of the method.

A new motion-based tracking algorithm for user interaction with hand-held mobile devices is presented. The idea is to allow mobile phone users to control the device simply by moving a finger in front of a camera. A novel combination of the Kalman filtering and the Expectation Maximization (EM) algorithm is utilized for estimation of two distinct motion components corresponding to the camera motion and the finger motion. The estimation is based on motion features, which are effectively extracted from the scene for each image frame. The performance of the technique is evaluated in experiments which show the usefulness of the approach. The method can be applied also when some conventional finger tracking techniques such as color segmentation and background subtraction can not be used.