A Real-Time Demonstrator for Video-Based Recognition of Dynamic Head Gestures, Using Discrete Hidden Markov Models
Frank Althoff, Gregor McGlaun, M. Lang, Gerhard Rigoll · 2003
This work describes a powerful demonstrator of a videobased approach for detecting and classifying dynamic head gestures. The head of the user is localized via a combination of color- and shape-based segmentation. For a continuous feature extraction, we use a template matching of the nose bridge in combination with selected features derived from the optical flow. The core classification unit consists of discrete Hidden Markov Models (DHMMs). We extensively tested the system in two different domains (desktop Virtual-Reality and automotive environment). In the current state of development, six different gestures can be classified with an overall recognition rate of 97.3 % in the VR, and 95.5% in the automotive environment, respectively. The approach works absolutely independent from the image background and additional gesture types can easily be integrated. 1.