Neural Network Performance Analysis for Real Time Hand Gesture Tracking Based on Hu Moment and Hybrid Features

Authors

Abstract:

This paper presents a comparison study between the multilayer perceptron (MLP) and radial basis function (RBF) neural networks with supervised learning and back propagation algorithm to track hand gestures. Both networks have two output classes which are hand and face. Skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in different background (simple and complex) with different illumination of environment to detect face, hand and its gesture. The number of training and testing samples in networks are equal and the set of binary images obtained from skin detection method is used to train the networks. Hand gestures are 6 cases which are tracked and they were not recognized. Both left and right hands has been trained to the network. Network features are based on the image transforms and they should not relate to deformation, size and rotation of hand. Since some of the features are in common with each other so a new method is applied to reduced calculation of input vector. Result shows that MLP has high accuracy and higher speed in tracking hand gesture in different background with minimum average error but it has a lower speed in training and convergence compare to the RBF in its final average error.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

neural network performance analysis for real time hand gesture tracking based on hu moment and hybrid features

this paper presents a comparison study between the multilayer perceptron (mlp) and radial basis function (rbf) neural networks with supervised learning and back propagation algorithm to track hand gestures. both networks have two output classes which are hand and face. skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...

full text

A Neural Network based Real Time Hand Gesture Recognition System

Hand Gesture is habitually used in every day life style. It is so natural way to communicate. Hand gesture recognition method is widely used in the application area of Controlling mouse and/or keyboard functionality, mechanical system, 3D World, Manipulate virtual objects, Navigate in a Virtual Environment, Human/Robot Manipulation and Instruction Communicate at a distance. This paper introduce...

full text

Real Time Hand Tracking and Gesture Rec

This paper outlines a system design and implementation of a 3D input device for graphical applications which uses real time hand tracking and gesture recognition to provide the user with an intuitive interface for tomorrow’s applications. Point Distribution Models (PDMs) have been shown to be successful at tracking deformable objects . This system demonstrates how these ‘smart snakes’ can be us...

full text

Real Time Hand Gesture Recognition Including Hand Segmentation and Tracking

In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, mot...

full text

Real-time Vision-based Hand Gesture Recognition Using Sift Features

This paper introduces a new algorithm based on machine vision for the recognition of hand gesture. In step 1, the Microsoft Kinect sensor is used to capture color images and depth. User’s hand detected by eliminating the background and rescaling image. In the next step, “Scale-invariant feature transform (SIFT)” algorithm is used for the feature extraction. The extracted feature vectors are bui...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 03  issue 02

pages  61- 72

publication date 2014-04-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023