Multi-signal gesture recognition using temporal smoothing hidden conditional random fields
Name
fg2011-Multi-Signal.pdf
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3.76 MB
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Adobe PDF
Checksum (MD5)
445ef36fc7ad00e50e69e2acf15a0b4c
Author(s)
Song, Yale
Demirdjian, David
Davis, Randall
Date Issued
March 2011
Journal
IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011
Publisher
Institute of Electrical and Electronics Engineers
Citation
Song, Yale, David Demirdjian, and Randall Davis. Multi-signal Gesture Recognition Using Temporal Smoothing Hidden Conditional Random Fields. In Face and Gesture 2011, 388-393. Institute of Electrical and Electronics Engineers, 2011.
Version
Author's final manuscript
Abstract
We present a new approach to multi-signal gesture recognition that attends to simultaneous body and hand movements. The system examines temporal sequences of dual-channel input signals obtained via statistical inference that indicate 3D body pose and hand pose. Learning gesture patterns from these signals can be quite challenging due to the existence of long-range temporal-dependencies and low signal-to-noise ratio (SNR). We incorporate a Gaussian temporal-smoothing kernel into the inference framework, capturing long-range temporal-dependencies and increasing the SNR efficiently. An extensive set of experiments was performed, allowing us to (1) show that combining body and hand signals significantly improves the recognition accuracy; (2) report on which features of body and hands are most informative; and (3) show that using a Gaussian temporal-smoothing significantly improves gesture recognition accuracy.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/FG.2011.5771431
