NDLI: Possibility theory based continuous Indian Sign Language gesture recognition

Content Provider	IEEE Xplore Digital Library
Author	Baranwal, N. Tripathi, K. Nandi, G.C.
Copyright Year	2015
Description	Author affiliation: Robot. & Artificial Intell. Lab., Indian Inst. of Inf. Technol., Allahabad, India (Baranwal, N.; Tripathi, K.; Nandi, G.C.)
Abstract	Today sign language is a vibrant field of research because it helps us to establish communication between hearing impaired community and normal community. In this paper, we proposed a novel continuous Indian Sign Language (ISL) gesture recognition technique where possibility theory (PT) has been applied. Preprocessing and extraction of overlapping frames (start and end point of each gesture) are the major issues which is being covered in this paper using background modeling and noble gradient method. Overlapping frames are helpful for fragmenting a continuous ISL gesture into isolated gestures. These isolated gestures are further processed and classified. During the segmentation process some of the gesture structures like shape and orientation of hand are deformed. A novel concept of wavelet descriptor has been applied here for extracting correct features of these deformed features and combine with the other two features (orientation and speed). Wavelet descriptor is very generic for finding a moment invariant features from the shape of the hand due to its multiresolution property. 3 dimensional feature vectors (orientation, speed and moment) are parellely combined and classified using possibility theory. Possibility theory is really suited for handling uncertainty as well as the precision present between intermediate frames. Experiments are performed on 10 sentences of continuous ISL having 1000 samples. This data set has been created in Robotics and AI laboratory, Indian Institute of Information Technology, Allahabad, India, where 4 persons are used for training and 6 persons are used for testing. From analysis of results we found that our proposed approach gives 92% classification results on continuous ISL. A classified isolated ISL gestures are combined for generating a judgment of conviction in the form of text or words.
Starting Page	1
Ending Page	5
File Size	770152
Page Count	5
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7372806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Random access memory Ferroelectric films Nonvolatile memory Face Markov processes Computational modeling computer vision Gesture recognition possibility theory wavelet descriptor
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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