NDLI: Depth data and fusion of feature descriptors for static gesture recognition

Content Provider	IET Digital Library
Author	Sharma, Prachi Anand, Radhey Shyam
Abstract	In this study, the authors propose a novel methodology for static gesture recognition in a complex background using only depth map from Microsoft's Kinect camera. Four different types of features are extracted and analysed on two public static gesture datasets. The features extracted from the segmented hand are geometrical, local binary patterns, number of fingers (Num) raised in a gesture and distance of hand palm centre from the fingertips and the valley between the fingers. The hand region is first segmented from the image using depth data followed by the forearm removal. Four multi-class support vector machine (SVM) kernels are also compared and used for recognition of gestures with extracted feature vector as an input. The experimental results achieved recognition accuracy of 99 and 95.7 % on two public complex static gesture datasets using Gaussian SVM kernel function as a classifier. The proposed approach is found to be comparable and even outperforms some of the state-of-the-art techniques in terms of high recognition accuracies, even after using a single cue for hand segmentation and extraction of features in the complex background which results in non-dependency on too many cues and much hardware.
Starting Page	909
Ending Page	920
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0230
Journal	IET Image Processing
Publisher Date	2020-01-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Complex Background Computer Vision And Image Processing Technique Depth Data Depth Map Extracted Feature Vector Feature Descriptors Feature Extraction Gaussian SVM Kernel Function Gesture Recognition Hand Palm Centre Hand Region Hand Segmentation High Recognition Accuracies Image Motion Analysis Image Recognition Image Segmentation Knowledge Engineering Technique Local Binary Pattern Microsoft's Kinect Camera Multiclass Support Vector Machine Kernels Optical, Image And Video Signal Processing Public Complex Static Gesture Datasets Public Static Gesture Datasets Recognition Accuracy Segmented Hand Static Gesture Recognition Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hand gesture recognition using DWT and F-ratio based feature descriptor

Recognition of complex static hand gestures by using the wristband-based contour features

Hand gesture recognition using multiclass support vector machine.

Indian sign language recognition using SVM

Hand gesture recognition based on depth image using kinect sensor

Hand gesture recognition using fourier descriptors

HGR-Net: a fusion network for hand gesture segmentation and recognition

Static gesture segmentation technique based on improved Sobel operator

Discriminating features learning in hand gesture classification

Depth data and fusion of feature descriptors for static gesture recognition

Similar Documents

Hand gesture recognition using DWT and F-ratio based feature descriptor

Recognition of complex static hand gestures by using the wristband-based contour features

Hand gesture recognition using multiclass support vector machine.

Indian sign language recognition using SVM

Hand gesture recognition based on depth image using kinect sensor

Hand gesture recognition using fourier descriptors

HGR-Net: a fusion network for hand gesture segmentation and recognition

Static gesture segmentation technique based on improved Sobel operator

Discriminating features learning in hand gesture classification

Depth data and fusion of feature descriptors for static gesture recognition