NDLI: Dynamic Sign Language Recognition for Smart Home Interactive Application Using Stochastic Linear Formal Grammar

Content Provider	IEEE Xplore Digital Library
Author	Abid, M.R. Petriu, E.M. Amjadian, E.
Copyright Year	1963
Abstract	This paper presents the state-of-the art dynamic sign language recognition (DSLR) system for smart home interactive applications. Our novel DSLR system comprises two main subsystems: an image processing (IP) module and a stochastic linear formal grammar (SLFG) module. Our IP module enables us to recognize the individual words of the sign language (i.e., a single gesture). In this module, we used the bag-of-features (BOFs) and a local part model approach for bare hand dynamic gesture recognition from a video. We used dense sampling to extract local 3-D multiscale whole-part features. We adopted 3-D histograms of a gradient orientation descriptor to represent features. The k-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was conducted using the BOFs and nonlinear support vector machine methods. We used a multiscale local part model to preserve temporal context. The SLFG module analyzes the sentences of the sign language (i.e., sequences of gestures) and determines whether or not they are syntactically valid. Therefore, the DSLR system is not only able to rule out ungrammatical sentences, but it can also make predictions about missing gestures, which, in turn, increases the accuracy of our recognition task. Our IP module alone seals the accuracy of 97% and outperforms any existing bare hand dynamic gesture recognition system. However, by exploiting syntactic pattern recognition, the SLFG module raises this accuracy by 1.65%. This makes the aggregate performance of the DSLR system as accurate as 98.65%.
Sponsorship	IEEE Instrumentation and Measurement Society Bureau international des poids et mesures IEEE International Bureau of Weights and Measures (BIPM) URSI NBS Agency Ind. Sci. & Technol., Min. Int. Trade & Ind. Sci. Council Japan International Union of Pure and Applied Physics (IUPAP) NIST NRCC National Conference of Standards Laboratories (NCSL) Allen Osborne Associates Andeen-Hagerling, Inc. Ballantine Lab. Inc. Clarke-Hess Communications Research Corp. Fluke Corporation Guildline Instruments Hewlett-Packard Co. Julie Res. Lab. Inc. Keithley Instruments, Inc. Measurements International Ltd. Canada QuadTech Inc. Quantum Design Inc. Rotek Instrum. Corp. Tegam Inc. Tektronix Inc
Starting Page	596
Ending Page	605
Page Count	10
File Size	1953520
File Format	PDF
ISSN	00189456
Volume Number	64
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gesture recognition Feature extraction Assistive technology IP networks Grammar Testing Accuracy syntactic pattern recognition Dynamic hand gesture dynamic sign language formal languages image processing (IP) local part model machine learning stochastic linear formal grammar (SLFG)
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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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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