UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 13 | Issue 3 | March 2026

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Published in:

Volume 6 Issue 2
February-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1902164


Registration ID:
196466

Page Number

433-437

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Title

Human Body Gesture Recognition System using concepts of Neural Network

Abstract

Human Body Gesture Recognition System using concepts of Neural Network. This is for developing and implemented an experimental setup consisting of a humanoid robot/android able to recognize and execute in real time all the arm gestures of the Dynamic Gesture Language (DGL) in similar way as humans do. Our DGLR system comprises two main subsystems: an image processing (IP) module and a linguistic recognition system (LRS) module. The IP module enables recognizing individual DGL gestures. In this module, we use the bag-of-features (BOFs) and a local part model approach for dynamic gesture recognition from images. Dynamic gesture classification is conducted using the BOFs and nonlinear support-vector-machine (SVM) methods. The multi scale local part model preserves the temporal context. The IP module was tested using two databases, one consisting of images of a human performing a series of dynamic arm gestures under different environmental conditions and a second database consisting of images of an android performing the same series of arm gestures. The linguistic recognition system (LRS) module uses a novel formal grammar approach to accept DGL-wise valid sequences of dynamic gestures and reject invalid ones. LRS consists of two subsystems: one using a Linear Formal Grammar (LFG) to derive the valid sequence of dynamic gestures and another using a Stochastic Linear Formal Grammar (SLFG) to occasionally recover gestures that were unrecognized by the IP module. Experimental results have shown that the DGLR system had a slightly better overall performance when recognizing gestures made by a human subject (98.92% recognition rate) than those made by the android (97.42% recognition rate).

Key Words

Arm Gesture, Neural Network, Pattern Recognition, Ten-fold Cross Validation etc.

Cite This Article

"Human Body Gesture Recognition System using concepts of Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.433-437, February-2019, Available :http://www.jetir.org/papers/JETIR1902164.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Human Body Gesture Recognition System using concepts of Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp433-437, February-2019, Available at : http://www.jetir.org/papers/JETIR1902164.pdf

Publication Details

Published Paper ID: JETIR1902164
Registration ID: 196466
Published In: Volume 6 | Issue 2 | Year February-2019
DOI (Digital Object Identifier):
Page No: 433-437
Country: INDIA, INDIA, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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