基于皮肤模板和改进HMM的自动人脸识别系统
基于皮肤模板和改进HMM的自动人脸识别系统
第25卷第1期2008年1月
深圳大学学报理工版
JOURNALOFSHENZHENUNIVERSITYSCIENCEANDENGINEERING
VnL25No.1
Jan.2008
文章编号:1000-2618(2008)01.0071.05
【电子光学与信息工程】
Automaticfacerecognitionbased
andimproved
SHENLin—iinand
on
skinmasking
HMM
Zhong
MING
CollegeofSoftwareShenzhenUniversityShenzhen518060P.R.China
Abstract:AnewhiddenMarker
module(HMM)basedfacerecognitionsystemispresentedinthispaper.Face
a
imageswereextractedautomaticallyfromlivevideoscapturedbyrealtimeusing
all
CreativeWebCam,andfaceswererecognizedin
improvedHMMfacerecognitionalgorithm.AfastfacedetectionalgorithmusingboostedHaar-like
re-
featureswasappliedinitiallytodetectfacesregionsfromthevideostream.Thedetectedfaceregionwasfurtherfinedby
a
skincolormaskingmoduletoachievemoreaccuratefaceposition.ToimprovetheaccuracyofHMMbased
facerecognitionalgorithm,discretewavelettransform,insteadofdiscretecosinetransform,wasusedtoextractob—servationsequencesforHMM。Experimentswereconductedusingtwofacedatabases:theORLdatabaseandtheNottinghamcolorfaceimagedatabase.Theresults
accuracy
on
bothdatabasesshowthattheproposedmethod
a
can
improvethe
bymorethansixpercents.Furtherimprovementhasbeenobservedwhenskinmaskingmoduleisusedto
refinethedetectedfaceKeywords:face
region.
detection;colorimageprocessing;biometrics;hiddenMarkovmodel
recognition;face
CLCNumber:TP391:TP751
Automated
Documentcode:A
a
facerecognitionsystemshave
as
widerangeofim—thesystem.Itsaccuracyismask
as
a
improved
using
to
a
fastskincolorbased
portantapplicationssuchvideosurveillance,crimeprevention
postprocessingprocedure
to
locatethefaceareaaccu-
anddetection.Acompletefacialimagerecognitionsystemshouldbeable
sanle
rately.Duefaces
Call
the
accuracyofthedetectedfaces,thedetectednormalized.
to
to
detectfacesin
a
givenimageand
recognize
thematthewill
beeasily
time.Oncethefacepositionis
fromthe
determined,facial
inboth
area
Major
approaches
face
recognitionincludegraphmate—
be
extracted
image,normalized
acaleandoften-
hing,neuralnetworksandhiddenMarkovmodels(HMM).An
thedynamiclinkarchitecturefirst
tation,andpassed
onto
thefacerecognizer.
Rowley'sneural
of
a
exampleofgraphmatchingis
Atypicalfacedetectionsystemis
basedfacenetworks
networl【-ofneural
proposedbyVonderMalsburg。“.Morerecentlythefacebunch
graph‘“iswavelets
detector[1|.Thedetectorconsists
by
a
set
proposedapplied
toat
copewithvariationsinfaceimages.Gabor
manuallyselectedftducialseveralimagesof
a
a
trained
largetraining
set
a
offaceimagesand
non-
are
points(eyes,
the
faceimages.Violarithmusingface
a
et
al[2J
proposedrapid
a
objectdetectionalgo-
cascadeofface/non -
mouth,nose,etc.)of
refereed
to
as
faceandtheresults.
set
ofHaar likefeatureand
jeta,arepackedin
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