An HMM-based threshold model approach for gesture recognition

Cited 369 time in webofscience Cited 0 time in scopus
  • Hit : 359
  • Download : 1223
The task of automatic gesture recognition is highly challenging due to the presence of unpredictable and ambiguous nongesture hand motions. In this paper, a new method is developed using the Hidden Markov Model based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14 percent reliability.
Publisher
IEEE COMPUTER SOC
Issue Date
1999-10
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.21, no.10, pp.961 - 973

ISSN
0162-8828
URI
http://hdl.handle.net/10203/12130
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 369 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0