Recognition of alphabetical hand gestures using hidden Markov model

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The use of hand gesture provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help achieve easy and natural comprehension for HCI. Many methods for hand gesture recognition using visual analysis have been proposed such as syntactical analysis, neural network (NN), and hidden Markov model (HMM)s. In our research, HMMs are proposed for alphabetical hand gesture recognition. In the preprocessing stage, the proposed approach consists of three different procedures for hand localization, hand tracking and gesture spotting. The hand location procedure detects the candidated regions on the basis of skin color and motion in an image by using a color histogram matching and time-varying edge difference techniques. The hand tracking algorithm finds the centroid of a moving hand region, connect those centroids, and produces a trajectory. The spotting algorithm divides the trajectory into real and meaningless gestures. In constructing a feature database, the proposed approach uses the weighted rho-phi-nu feature code, and employ a k-means algorithm for the codebook of HMM. In our experiments, 1,300 alphabetical and 1,300 untrained gestures are used for training and testing, respectively. Those experimental results demonstrate that the proposed approach yields a higher and satisfactory recognition rate for the images with different sizes, shapes and skew angles.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
1999
Language
English
Article Type
Article
Citation

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, v.E82A, no.7, pp.1358 - 1366

ISSN
0916-8508
URI
http://hdl.handle.net/10203/74633
Appears in Collection
CS-Journal Papers(저널논문)
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