Please use this identifier to cite or link to this item:
Title: Hand Gesture Recognition Using Local Histogram Feature Descriptor
Authors: Reddy, Dandu Amarnatha
Sahoo, Jaya Prakash
Ari, Samit
Keywords: Feature extraction
Hand gesture recognition
Human computer interaction
Local histogram
Support Vector Machine
Issue Date: May-2018
Citation: 2nd International Conference on Trends in Electronics and Informatics (ICOEI 2018), Tamil Nadu, India, 11 - 12 May, 2018
Abstract: Hand gesture recognition system is widely used in the development of human-computer interaction. The vision based hand gesture recognition is achieved by the following steps: preprocessing, feature extraction and classification. The aim of preprocessing stage is to localize the hand region from the image frame. The Laplacian of Gaussian filtering technique along with zero crossing detector is applied on hand gesture images to detect the edges of hand region. This paper proposes a novel feature extraction technique, which is based on local histogram feature descriptor (LHFD). The proposed feature is extracted by finding the local histogram of the gray scale gesture image. This technique uses the whole region of the hand to extract the features. The proposed method is invariant to the scaling and illumination. Two standard datasets viz. Massey University gesture dataset (MUGD) and Jochen Triesch static hand posture database are used to evaluate the recognition performance of the proposed technique. The gesture recognition performance of the proposed technique is 99.5% and 95% on Massey University gesture dataset and Triesch dataset respectively, using multi-class support vector machine (SVM) classifier.
Description: Copyright of this document belongs to proceedings publisher.
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2018_ICOEI_DAReddy_Hand gesture.pdfConference Paper916.56 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.