Correlation analysis of facial features and sign gestures

In this paper we focus on the potential correlation of the manual and the non-manual component of sign language. This information is useful for sign language analysis, recognition and synthesis. We are mainly concerned with the application for sign synthesis. First we extracted features that represent the manual and non-manual component. We present a simple but robust method for the hand tracking to obtain a 2D trajectory representing a portion of the manual component. The head is tracked via Active Appearance Model. We introduce initial experiments to reveal the relationship between these features. The procedure is verified on the corpus of isolated signs from Czech Sign Language. The results imply that the components of sign language are correlated. The most correlated signals are the vertical movement of head and hands.