Invariant features for 3-D gesture recognition

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http://doi.ieeecomputersociety.org/10.1109/AFGR.1996.557258

Second IEEE International Conference ...

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	ASCII Text	x
	L.W. Campbell, D.A. Becker, A. Azarbayejani, A.F. Bobick, A. Pentland, "Invariant features for 3-D gesture recognition," Automatic Face and Gesture Recognition, IEEE International Conference on, pp. 157, Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96), 1996.

	BibTex	x
	@article{ 10.1109/AFGR.1996.557258, author = {L.W. Campbell and D.A. Becker and A. Azarbayejani and A.F. Bobick and A. Pentland}, title = {Invariant features for 3-D gesture recognition}, journal ={Automatic Face and Gesture Recognition, IEEE International Conference on}, volume = {0}, year = {1996}, isbn = {0-8186-7713-9}, pages = {157}, doi = {http://doi.ieeecomputersociety.org/10.1109/AFGR.1996.557258}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Automatic Face and Gesture Recognition, IEEE International Conference on TI - Invariant features for 3-D gesture recognition SN - 0-8186-7713-9 SP EP A1 - L.W. Campbell, A1 - D.A. Becker, A1 - A. Azarbayejani, A1 - A.F. Bobick, A1 - A. Pentland, PY - 1996 KW - feature extraction; gesture recognition; feature vectors; 3D data; stereo video cameras; HMMs; learning; recognition; partial rotational invariance; velocity features VL - 0 JA - Automatic Face and Gesture Recognition, IEEE International Conference on ER -

L.W. Campbell, Media Lab., MIT, Cambridge, MA, USA

D.A. Becker, Media Lab., MIT, Cambridge, MA, USA

A. Azarbayejani, Media Lab., MIT, Cambridge, MA, USA

A.F. Bobick, Media Lab., MIT, Cambridge, MA, USA

A. Pentland, Media Lab., MIT, Cambridge, MA, USA

Ten different feature vectors are tested in a gesture recognition task which utilizes 3D data gathered in real-time from stereo video cameras, and HMMs for learning and recognition of gestures. Results indicate velocity features are superior to positional features, and partial rotational invariance is sufficient for good performance.

Index Terms:

feature extraction; gesture recognition; feature vectors; 3D data; stereo video cameras; HMMs; learning; recognition; partial rotational invariance; velocity features

Citation:

L.W. Campbell, D.A. Becker, A. Azarbayejani, A.F. Bobick, A. Pentland, "Invariant features for 3-D gesture recognition," fg, pp.157, Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96), 1996

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