Research Paper HAND POSTURE RECOGNITION - ijerst · HAND POSTURE RECOGNITION USING SVM TECHNIQUES ... Primary interface of OpenCV is in C++. There are also C, Python and JAVA full

142

This article can be downloaded from http://www.ijerst.com/currentissue.php

Int. J. Engg. Res. & Sci. & Tech. 2014 K Praveen Kumar and Bhakkiyalakshmi R, 2014

HAND POSTURE RECOGNITION

USING SVM TECHNIQUES

K Praveen Kumar1* and Bhakkiyalakshmi R1

This paper presents a better and accurate way to human computer interaction methods. A realtime system is proposed and developed which needs no glove of sensors for the hand, still wecan directly interact with system. The implemented system is tested by demonstrating how wecan control device using the hand signs. Gesture recognition interfaces can be used as a naturalcommunication channel between humans and machines and give rise to a plethora of applicationssuch as hardware-free remote controls, sign language interpretation and other human welfareapplications.

Keywords: Hand Gesture, SVM, Erosion, Dilution, Morphological operations, AbsoluteDifference, Contour, Binary Thresholding

*Corresponding Author: K Praveen Kumar � [email protected]

INTRODUCTION

Evolution of technology and the development of

digital signal processing in combine providing a

way to interact with the computers virtually. We

can personalize this development so that is useful

to simplify the human effort in real time. In present

scenario gesture recognition systems are widely

occupying most of the daily routine works, as we

can make use of the device on the go. These

systems are like ALERT and FORGOT. We can

use this hand sighs in controlling mouse, keyboard

functions. Rapid and simple conversion of hand

movements into 3D computer space for the

purposes of computer animation is another area

of interest.

1 SRM University, Chennai, India.

Int. J. Engg. Res. & Sci. & Tech. 2014

ISSN 2319-5991 www.ijerst.com

Vol. 3, No. 2, May 2014

© 2014 IJERST. All Rights Reserved

Research Paper

The hardware used for computer games of

present generation slowly replacing with the sign

language. Control of mechanical systems such

as robotics. Using the hand to remotely control a

manipulator. The extensive experiments

demonstrate that our hand gesture recognition

system is accurate efficient robust to hand

articulations, distortions and orientation or scale

changes, and can work in uncontrolled

environments with cluttered backgrounds

(Malassiotis and Strintzis, 2008) and lighting

conditions. The superiority of our system is further

demonstrated in two real-life HCI applications (El-

Sawal et al., 2008).

There are many pattern recognition

143



techniques. However, the problem with gesture

recognition (Nasser et al., 2011) is that the inputs

usually consist of multi-dimensional and time-

varying Data. If the input data were either multi-

dimensional or time varying, existing pattern

recognition techniques could easily be applied. A

common approach to handling such data is to

divide each gesture into short phases, and then

to recognize each phase using a pattern

recognition technique for multi-dimensional data.

In order to be applicable to current PCs and mobile

devices, a gesture recognition system should be

based on existing and common hardware such

as low-resolution web cams or mobile-integrated

cameras. It is also desired that the System will

be able to operate under non-uniform background,

lighting and noise conditions. Another requirement

for the gesture recognition system is to be

computationally non-intensive in order to be

suitable for real-time classification.

We use an SVM to learn the transition

conditions for each state. The SVM is a popular

pattern recognition technique with supervised

learning. Since it divides the feature space for

each class, the SVM can handle unknown data

well, although it is not suited to grouping sample

data. In order to classify the dynamic hand

gestures under noisey background, motion history

image and different groups of novel Haar-like

features (Kolsch and Turk, 2004) are investigated

to classify the dynamic up, down, left, and right

hand gestures. A simple efficient algorithm using

Support Vector Machine is developed. These

defined hand gestures are intuitive and easy for

user to control most home appliances. The

primary idea of the SVMs is to implement a

hyperplane (Hirotaka et al., 2001) as the decision

plane, which separates the positive and negative

binary classes with the largest margin, which is

related to minimizing the VC dimension of SVM.

In a binary classification problem where feature

extraction is initially performed.

BLOCK DIAGRAM AND

EXPLANATION

Components Description

Transmitter Section

A Camera is used to capture the frames from

real time. Most of the technological device comes

Figure 1: Transmitter Section

with integrated cameras however we can add the

externally to make it more flexible.

PC is the module where all the processing like

storing temporary frames, developing pattern

recognition algorithm takes place. It has to be with

good processing speed and also has to support

the required development environment.

UART provides the physical communication

link between the PC and the hardware we are

developing. The MCU is the processing unit for

the hardware.

Encoder module improves error less

transmission over a noisy channel. We can use

NXM class devices based on our requirements.

RF transmitter suitable for short distance

communication. Speed and Accurate. The rate

144



of oscillation in the range of around 3 kHz to 300

GHz and 3 m to 30 m distance can be covered.

Receiver Section

RF receiver receives the signal sent by the

transmitter and decodes it.

Decoder helps to find the errors occurred in

the transmission.

Voice board is nothing but a sounding device.

It’s like an announcer of the recognized sign. Let’s

say when system identifies a sign as ONE it

speaks as ONE.

Most of the high end industrial application

devices have relays for their effective working.

Relays are simple switches which are operated

both electrically and mechanically. Relays consist

of an electromagnet and also a set of contacts.

The switching mechanism is carried out with the

help of the electromagnet. There are also other

operating principles for its working. But they differ

according to their applications. We use Single

Pole Single Throw (SPST) type here for

implementation. However we can choose our

desired type based on the requirement.

SOFTWARE ENVIRONMENT

The required software environment is provided

by the Programs named OpenCV and

Figure 2: Reciever Section

MICROSOFT VISUAL STUDIO.

OpenCV is an open source C++ library for

image processing and computer vision, originally

developed by Intel and now supported by Willow

Garage. It is free for both commercial and non-

commercial use. Therefore it is not mandatory

for your OpenCV applications to be open or free.

It is a library of many inbuilt functions mainly aimed

at real time image processing. Now it has several

hundreds of image processing and computer

vision algorithms which make developing

advanced computer vision applications easy and

efficient.

Key Features: Optimized for real time imageprocessing and computer vision applications.

Primary interface of OpenCV is in C++. There

are also C, Python and JAVA full interfaces.

OpenCV applications run on Windows, Android,

Linux, Mac and Ios. Optimized for Intel

processors.

OpenCV(http://docs.opencv.org/master/doc/

user_guide/user_guide.html) has a modular

structure. The main modules of OpenCV are

listed below

CORE: This is the basic module of OpenCV. Itincludes basic data structures (e.g. - Mat data

structure) and basic image processing functions.

This module is also extensively used by other

modules like highgui, etc.

Highgui: This module provides simple userinterface capabilities, several image and video

codecs, image and video capturing capabilities,

manipulating image windows, handling track bars

and mouse events and etc.

Imgproc: This module includes basic imageprocessing algorithms including image filtering,

image transformations, color space conversions

145



and etc.

Video: This is a video analysis module whichincludes object tracking algorithms, background

subtraction algorithms and etc. Objdetect: This

includes object detection and recognition

algorithms for standard objects. OpenCV is now

extensively used for developing advanced image

processing and computer vision applications. It

has been a tool for students, engineers and

researchers in every nook and corner of the world.

The library is very much rich for processing

real time images some of those important

functions and syntax are given below. However

you can find these functions in the official page of

the OpenCV as its free of licence.

cvCapture ();

Syntax: typedef struct CvCapture;

CvNamedWindow ();

Creates window with given name.

Syntax: Int cvNamedWindow (const char*name, int flags=CV_WINDOW_AUTOSIZE);

CvGrabFrame();

Grabs frame from camera or file.

Syntax: int cvGrabFrame( CvCapture* capture)

cvReleaseCapture();

Releases the CvCapture structure

Syntax: void cvReleaseCapture( CvCapture**capture);

WORKING

A camera is used to acquire the image in real

time and then sends to PC. It may be a integrated

to the system or we can add externally. We

cannot process images as soon as we got them

from camera in real time The image is stored

temporarily and processing section will the

recognize the hand gesture. First the trained xml

file must be lined in the program so that the

system first identifies the hand. Then we focus

on the hand part. We convert the hand image

into a fully binary image which is a black and white

version of the image. This also known as

histogram of the image in which all pixels must

either white or black. Then the system able to

recognize the gesture of the hand and the

corresponding signal is sent to the transmitter

section which is connected to PC. The transmitter

transmits with RF frequency. The receiver

receives the signals and control the device with

relay as per the hand gesture. The voice board

tells the identified gesture.

However real time noise is unpredictable so

we can implement some additional add-ons. First

we make sure that the object (Viola, 2001)

present in the frame is human for this we can

look for face. As the sensors are abundantly

available in the market we can go for a

temperature sensor to verify the human with body

temperature or we can go for a heartbeat sensor.

The noise can be minimized with image

processing techniques, Absolute Difference,Contour, and Binary Thresholding.

OBSERVED RESULTS

The number fingers in the frame can be identified

Figure 3: One Sign

146



show that the system can achieve satisfactory

real-time performance regardless of the frame

resolution size as well as high classification

accuracy under variable scale, orientation and

illumination conditions, and cluttered background.

Three important factors affect the accuracy of

the system, which are the quality of the webcam

in the training and testing stages, the number of

the training images. However, if detection of hand

gestures for computer animation (Du W and Li

H, 2000) is required then we must train the system

with thousands of gestures which is complex but

yields high accuracy. A system which relies on

both training and comparison of all gestures used

would not be sufficient for this task.

REFERENCES

1. Du W and Li H (2000), “Vision based gesture

recognition system with single camera”, In:

Proceedings of ICSP, pp. 1351–1357.

2. El-Sawah A, Georganas N, and Petriu E

(2008), “A prototype for 3-D hand tracking

and gesture estimation,” IEEE Trans.

Instrum. Meas., Vol. 57, No. 8, pp. 1627–

1636.

3. Hirotaka Iuchi, Sakashi Maeda and Naoyuki

Tsuruta (2001), “Gesture Recognition using

Self- Organizing Maps and Hidden Markov

Model”, IPSJ SIG Notes, Computer Vision

and Image Media, Vol. 2001, No. 36, pp. 127-

134.

4. ht tp: / /docs.opencv.org/master /doc/

user_guide/user_guide.html

5. Kolsch M and M. Turk, “Analysis of rotational

robustness of handdetection with a Viola-

Jones detector,” in Proc. 17th ICPR, 2004,

pp. 107–110.

Figure 4: Two Sign

Figure 5: Three Sign

Figure 6: Fist Sign

by using the number of peaks in the frame.

However if two nearest peaks are found the

system treats that case as a one finger. It is

necessary that when performing the gesture on

the frame sufficient distance must be engaged

between the fingers.

CONCLUSION

In this paper, we have described a real-time

system that consists of three modules: hand

detection and tracking using skin detection and

contour comparison algorithm, posture

recognition using multiclass SVM, Experiments

147



6. Malassiotis and Strintzis M G (2008), “Real-

time hand posture recognition using range

data”, Image and Vision Computing, Vol. 26,

pp. 1027 - 1037.

7. Nasser H Dardas and Nicolas D Georganas

(2001), “Real-Time Hand Gesture Detection

and Recognition Using Bag-of-Features and

Support Vector Machine Techniques”, IEEE

Trans.Instrum. Meas., Vol. 60, No. 11.

8. Viola P and Jones M (2001), “Rapid object

detection using a boostedcascade of simple

features”, In: Proceedings of the IEEE

Computer Society Conference on Computer

Vision and Pattern Recognition, pp. 511–518.

/ColorImageDict > /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 300 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile () /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False

/Description > /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ > /FormElements false /GenerateStructure true /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles true /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /NA /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /LeaveUntagged /UseDocumentBleed false >> ]>> setdistillerparams> setpagedevice

Documents

Research Paper HAND POSTURE RECOGNITION - ijerst · HAND POSTURE RECOGNITION USING SVM TECHNIQUES ... Primary interface of OpenCV is in C++. There are also C, Python and JAVA full