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E-mail:
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Introduction
Eyesarethemostimportantfeaturesofthehumanface.Soeffectiveusageofeyemovementsasacommunicationtechniqueinuser-to-computerinterfacescanfindplaceinvariousapplicationareas.
Eyetrackingandtheinformationprovidedbytheeyefeatureshavethepotentialtobecomeaninterestingwayofcommunicatingwithacomputerinahuman-computerinteraction（HCI）system.Sowiththismotivation,designingareal-timeeyefeaturetrackingsoftwareistheaimofthisproject.
Thepurposeoftheprojectistoimplementareal-timeeye-featuretrackerwiththefollowingcapabilities:
∙RealTimefacetrackingwithscaleandrotationinvariance
∙Trackingtheeyeareasindividually
∙Trackingeyefeatures
∙Eyegazedirectionfinding
∙Remotecontrollingusingeyemovements
InstructionstoRunandRebuildTrackEye
InstallationInstructions
1.Extract
TrackEye_Executable.zip
file.Beforerunning
TrackEye_636.exe,copythetwofiles
SampleHUE.jpg
andSampleEye.jpg
tothe
C:
\
folder.ThesetwofilesareusedforCAMSHIFTandTemplate-Matchingalgorithms.
2.Therearenootherstepstobefollowedbytheusertorunthesoftware.TherearenoDLLdependenciesasthesoftwarewasbuiltwiththeDLLsstaticallyincluded.
SettingstobeDonetoPerformaGoodTracking
SettingsforFace&
EyeDetection
UnderTrackEyeMenu-->
TrackerSettings
∙InputSource:
video
∙ClickonSelectfileandselect
..\Avis\Sample.avi
∙FaceDetectionAlgorithm:
HaarFaceDetectionAlgorithm
∙Check“TrackalsoEyes”checkBox
∙EyeDetectionAlgorithm:
AdaptivePCA
∙Uncheck“VarianceCheck”
∙NumberofDatabaseImages:
8
∙NumberofEigenEyes:
5
∙Maximumallowabledistancefromeyespace:
1200
∙Facewidth/eyetemplatewidthratio:
0.3
∙ColorSpace
typetouseduringPCA:
CV_RGB2GRAY
SettingsforPupilDetection
Check“Trackeyesindetails”andthencheck“Detectalsoeyepupils”.Click“AdjustParameters”button:
∙Enter“120”asthe“ThresholdValue”
∙Click“SaveSettings”andthenclick“Close”
SettingsforSnake
Check“Indicateeyeboundaryusingactivesnakes”.Click“Settingsforsnake”button:
∙Select
ColorSpace
touse:
∙SelectSimplethresholdingandenter100asthe“Thresholdvalue”
Background
Sofartherehasbeenalotofworkoneyedetectionandbeforetheproject,thepreviousmethodswerecarefullystudiedtodeterminetheimplementedmethod.Wecanclassifystudiesrelatedtoeyeintotwomaincategoriesaslistedbelow:
SpecialEquipmentBasedApproaches
Thesetypeofstudiesusethenecessaryequipmentwhichwillgiveasignalofsomesortwhichisproportionaltothepositionoftheeyeintheorbit.VariousmethodsthatarecurrentinuseareElectrooculography,Infra-RedOculography,Scleralsearchcoils.Thesemethodsarecompletelyoutofourproject.
ImageBasedApproaches
Imagebasedapproachesperformeyedetectionsontheimages.Mostoftheimagebasedmethodstrytodetecttheeyesusingthefeaturesoftheeyes.Methodsusedsofarareknowledge-basedmethods,feature-basedmethods（color,gradient）,simpletemplatematching,appearancemethods.Anotherinterestingmethodis“Deformabletemplatematching”whichisbasedonmatchingageometricaleyetemplateonaneyeimagebyminimizingtheenergyofthegeometricalmodel.
ImplementationofTrackEye
Theimplementedprojectisonthreecomponents:
1.Facedetection:
Performsscaleinvariantfacedetection
2.Eyedetection:
Botheyesaredetectedasaresultofthisstep
3.Eyefeatureextraction:
Featuresofeyesareextractedattheendofthisstep
FaceDetection
Twodifferentmethodswereimplementedintheproject.Theyare:
1.ContinuouslyAdaptiveMeans-ShiftAlgorithm
2.HaarFaceDetectionmethod
ContinuouslyAdaptiveMean-ShiftAlgorithm
AdaptiveMeanShiftalgorithmisusedfortrackinghumanfacesandisbasedonrobustnon-parametrictechniqueforclimbingdensitygradientstofindthemode（peak）ofprobabilitydistributionscalledthemeanshiftalgorithm.Asfacesaretrackedinvideosequences,meanshiftalgorithmismodifiedtodealwiththeproblemofdynamicallychangingcolorprobabilitydistributions.Theblockdiagramofthealgorithmisgivenbelow:
Haar-FaceDetectionMethod
ThesecondfacedetectionalgorithmisbasedonaclassifierworkingwithHaar-Likefeatures（namelyacascadeofboostedclassifiersworkingwithHaar-likefeatures）.Firstofallitistrainedwithafewhundredsofsampleviewsofaface.Afteraclassifieristrained,itcanbeappliedtoaregionofinterestinaninputimage.Theclassifieroutputsa"
1"
iftheregionislikelytoshowfaceand"
0"
otherwise.Tosearchfortheobjectinthewholeimage,onecanmovethesearchwindowacrosstheimageandcheckeverylocationusingtheclassifier.Theclassifierisdesignedsothatitcanbeeasily"
resized"
inordertobeabletofindtheobjectsofinterestatdifferentsizes,whichismoreefficientthanresizingtheimageitself.
EyeDetection
Twodifferentmethodswereimplementedintheproject:
1.Template-Matching
2.Adaptive
EigenEye
Method
Template-Matching
Template-Matchingisawell-knownmethodforobjectdetection.Inourtemplatematchingmethod,astandardeyepatterniscreatedmanuallyandgivenaninputimage,thecorrelationvalueswiththestandardpatternsarecomputedfortheeyes.Theexistenceofaneyeisdeterminedbasedonthecorrelationvalues.Thisapproachhastheadvantageofbeingsimpletoimplement.However,itmaysometimesbeinadequateforeyedetectionsinceitcannoteffectivelydealwithvariationinscale,poseandshape.
AdaptiveEigenEyeMethod
Adaptive
Methodisbasedonthewell-knownmethod
EigenFaces.Howeverasthemethodisusedforeyedetectionwenameditas“EigenEye
Method”.Themainideaistodecomposeeyeimagesintoasmallsetofcharacteristicsfeatureimagescalledeigeneyes,whichmaybethoughtofastheprincipalcomponentsoftheoriginalimages.Theseeigeneyesfunctionastheorthogonalbasisvectorsofasubspacecalledeyespace.Howeverweknowthatthe
eigenface
methodisnotscaleinvariant.Toprovidethescaleinvariancewecanresizetheeye-databaseoncewiththeinformationgatheredbythefacedetectionalgorithm（EyeWidth/FaceWidth?
0.35）,wecanprovidescale-invariantdetectionusingonlyonedatabase.
OpenCVFunctionsforObjectTrackingandDetection
OpenCV
Libraryoffersalotofimageprocessingandobjecttracking&
detectionlibraries.Themainfunctionusedintheseprojectsandtheirusagearegivenbelow:
SampleCodeforHaar-FaceTracking
Collapse
|
CopyCode
voidCTrackEyeDlg:
:
HaarFaceDetect（IplImage*img,CvBox2D*faceBox）
{
intscale=2;
IplImage*temp=cvCreateImage（cvSize（img->
width/2,img->
height/2）,8,3）;
CvPointpt1,pt2;
inti;
cvPyrDown（img,temp,CV_GAUSSIAN_5x5）;
#ifdefWIN32
cvFlip（temp,temp,0）;
#endif
cvClearMemStorage（storage）;
if（hid_cascade）
{
CvSeq*faces=cvHaarDetectObjects（temp,hid_cascade,storage,1.2,2,
CV_HAAR_DO_CANNY_PRUNING）;
NumOfHaarFaces=faces->
total;
if（NumOfHaarFaces>
0）
CvRect*r=（CvRect*）cvGetSeqElem（faces,0,0）;
pt1.x=r->
x*scale;
pt2.x=（r->
x+r->
width）*scale;
pt1.y=img->
height-r->
y*scale;
pt2.y=img->
height-（r->
y+r->
height）*scale;
#else
pt1.y=r->
pt2.y=（r->
faceBox->
center.x=（float）（pt1.x+pt2.x）/2.0;
center.y=（float）（pt1.y+pt2.y）/2;
size.width=（float）（pt2.x-pt1.x）;
size.height=（float）（pt1.y-pt2.y）;
}
cvShowImage（"
Tracking"
img）;
cvReleaseImage（&
temp）;
}
SampleCodeforCamShiftAlgorithm
//InputsforCamShiftalgorithm
IplImage*HUE=cvCreateImage（cvGetSize（SampleForHUE）,IPL_DEPTH_8U,1）;
extractHUE（SampleForHUE,HUE）;
//**ExtractHUEinformation
inthist_size=20;
floatranges[]={0,180};
float*pranges[]={ranges};
hist=cvCreateHist（1,&
hist_size,CV_HIST_ARRAY,pranges,1）;
cvCalcHist（&
HUE,hist）;
//CalculatehistogramofHUEpart
hueFrame=cvCreateImage（cvGetSize（CameraFrame）,IPL_DEPTH_8U,1）;
backProject=cvCreateImage（cvGetSize（CameraFrame）,IPL_DEPTH_8U,1）;
extractHUE（CameraFrame,hueFrame）;
while（trackControl!
=0）
extractHUE（CameraFrame,hueFrame）;
cvCalcBackProject（&
hueFrame,backProject,hist）;
//Probabilityisformed
//cvShowImage（"
Tester2"
backProject）;
cvCamShift（backProject,searchWin,cvTermCriteria（CV_TERMCRIT_EPS|
CV_TERMCRIT_ITER,15,0.1）,&
comp,&
faceBox）;
searchWin=comp.rect;
SampleCodeTemplateMatching
//TemplateMatchingforEyedetection
voidFace:
findEyes_TM（IplImage*faceImage,TrackingSettings*settings）
CvSizefaceSize;
faceSize=cvGetSize（faceImage）;
//LoadTemplatefromtheeyedatabase
CStringfileName;
//Nameofthetemplateforlefteye
fileName.Format（"
%s\\eye%d.jpg"
settings->
params->
DBdirectory,0）;
IplImage*eyeImage_Left=cvLoadImage（fileName,-1）;
DBdirectory,1）;
IplImage*eyeImage_Right=cvLoadImage（fileName,-1）;
IplImage*tempTemplateImg_Left;
IplImage*tempTemplateImg_Right;
IplImage*templa
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