NDLI: Kinect-based multimodal gesture recognition using a two-pass fusion scheme

Content Provider	IEEE Xplore Digital Library
Author	Pavlakos, Georgios Theodorakis, Stavros Pitsikalis, Vassilis Katsamanis, Athanasios Maragos, Petros
Copyright Year	2014
Description	Author affiliation: School of Electrical and Computer Engineering, National Technical University of Athens, Greece (Pavlakos, Georgios; Theodorakis, Stavros; Pitsikalis, Vassilis; Katsamanis, Athanasios; Maragos, Petros)
Abstract	We present a new framework for multimodal gesture recognition that is based on a two-pass fusion scheme. In this, we deal with a demanding Kinect-based multimodal dataset, which was introduced in a recent gesture recognition challenge. We employ multiple modalities, i.e., visual cues, such as colour and depth images, as well as audio, and we specifically extract feature descriptors of the hands' movement, handshape, and audio spectral properties. Based on these features, we statistically train separate unimodal gesture-word models, namely hidden Markov models, explicitly accounting for the dynamics of each modality. Multimodal recognition of unknown gesture sequences is achieved by combining these models in a late, two-pass fusion scheme that exploits a set of unimodally generated n-best recognition hypotheses. The proposed scheme achieves 88.2% gesture recognition accuracy in the Kinect-based multimodal dataset, outperforming all recently published approaches on the same challenging multimodal gesture recognition task.
Starting Page	1495
Ending Page	1499
File Size	252187
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025299
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Gesture recognition Speech Skeleton Feature extraction Visualization Three-dimensional displays multimodal fusion multimodal gesture recognition HMMs speech recognition
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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