Static Hand Posture Recognition Based on an Implicit Shape Model
Date Issued
2010
Date
2010
Author(s)
Liu, Che-Wei
Abstract
Hand gesture recognition has become increasingly popular in Human-Computer Interaction (HCI) research as gestures provide a natural way of communication. Previous research has focused on searching a fixed size sub-window by evaluating a subspace of feature space that is found from machine learning algorithms such as AdaBoost. In recent years, however, local features have become increasingly popular as they offer robustness in illumination of the environment, scale, and rotational invariance of the hand itself. In this thesis, we describe a novel method of static hand posture recognition that is based on an Implicit Shape Model (ISM) of local features. We find improvement in recognition accuracy over former methods. In addition, our algorithm enhances the sliding-window paradigm by providing useful information such as hand orientation and rotational invariance. The execution time of the algorithm is also provided in order to assess its potential to be incorporated into a near real-time posture recognition application or a hand gesture system module.
Subjects
Implicit shape model
hand posture recognition
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