Adaptive motion-based gesture recognition interface for mobile phones

In this paper, we introduce anew vision based interaction technique for Mobile phones. The user operates the interface by simply moving a finger in front of a camera. During these movements the finger is tracked using a method that embeds the Kalman filter and Expectation Maximization (EM) algorithms. Finger movements are interpreted as gestures using Hidden Markov Models (HMMs). This involves first creating a generic model of the gesture and then utilizing unsupervised Maximum a Posteriori (MAP) adaptation to improve the recognition rate for a specific user. Experiments conducted on a recognition task involving simple control commands clearly demonstrate the performance of our approach.