Feature extraction: hand shape, hand position and hand trajectory path - Bilal, Sara Mohammed Osman Saleh; Akmeliawati, Rini (IIUM Press, 2011)
Vision-based hand posture detection and tracking is an important issue for Human to Computer Interaction applications. The performance of recognition system fIrst depends on the process of getting effIcient features to represent pattern characteristics [1]. There is no algorithm which shows how to select the representation or choose the features [2] so the selection of features will depend on the application. There are many different methods to represent 2-D images such as boundary, topological, shape grammar, description of similarity etc. [2-4]. Features should be chosen so that they are intensive to noise-like variation in pattern and keep the number of feature small for easy computation [5]. Hand posture shape features, motion trajectory feature and hand position with respect to other human upper body parts play an important role within the preparation stage of the gesture before recognition. In this chapter, features have been extracted from hand posture closed contours, hand posture trajectory and hand position has been identifIed. Algorithms have been developed for extracting these features after segmenting the head and the two hands. These extracted features can be attached to a recognizer such as Support Vector machine, Hidden Markov Model, etc. for hand gesture recognition.