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Face Recognition

Jonathan Bruno

Department of Computing Sciences

Villanova University, Villanova, PA 19!"

CSC #99 $ Computing %esearch &opics 'onathan()runo*villanova(e+u

Abstract

Biometrics is the automate+ i+entification of a person )ase+ on physical traits(

ne )iometric -hich has receive+ consi+era)le attention in recent years is facerecognition( .ace recognition is consi+ere+ to )e one of the most challenging )iometrics

 )ecause it +epen+s on variations in image /uality, orientation, an+ the su)'ect0s

appearance( &his paper +iscusses current implementations using D or #D )ase+

recognition( D recognition achieves generally impressive results( 2o-ever, accuracy+ecreases +rastically -hen the images )eing compare+ have significant variations(

Currently, there is much research )eing +one in the area of #D recognition -hich hopes toimprove upon the inherent limitations of D recognition(

1. Introduction

.ace recognition is an attractive )iometric for use in security applications( .acerecognition is non3intrusive, it can )e performe+ -ithout the su)'ect0s 4no-ing( &his has

 )ecome particularly important in mo+ern times )ecause +eman+ for enhance+ security is

in pu)lic interest(

2. Facial Recognition Approaches

2.1 Eigenface-based RecognitionD face recognition using eigenfaces is one of the ol+est types of face

recognition( &ur4 an+ Pentlan+ pu)lishe+ the groun+)rea4ing 5.ace %ecognition Using

6igenfaces7 in 1991 81( &he metho+ -or4s )y analy:ing face images an+ computingeigenfaces, -hich are faces compose+ of eigenvectors( %esults o)taine+ )y comparing

eigenfaces are use+ to i+entify the presence of a face an+ its i+entity(

&here is a five step process involve+ in the system +evelope+ )y &ur4 an+Pentlan+( .irst, the system nee+s to )e initiali:e+ )y fee+ing it a training set of face

images( &hese are use+ to +efine the face space -hich is a set of images that are face3li4e(

 ;e

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systems +o not capture the actual si:e of the face, -hich is a fun+amental pro)lem 8>(

&hese limits affect the techni/ue0s application -ith security cameras )ecause frontal shots

an+ consistent lighting cannot )e relie+ upon(

2.2 3D Face Recognition

#D face recognition is e( #D systems generate #D mo+els of faces an+ compare them( &hese systems

are more accurate )ecause they capture the actual shape of faces( S4in te

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Figure 1. Staring at the faces in the green circles -ill cause one to misi+entify the central face -ith the facescircle+ in re+ 8!(

9( Pigmentation cues are at least as important as shape cues(

1( Color cues play a significant role, especially -hen shape cues are +egra+e+(11( Contrast polarity inversion +ramatically impairs recognition performance,

 possi)ly +ue to compromise+ a)ility to use pigmentation cues( See .igure (

Figure 2. Photograph +uring the recor+ing of 5e Are the orl+(7 Several famous artists are in the picture

inclu+ing %ay Charles, ionel %itchie, Stevie on+er, ichael Jac4son, an+ Billy Joel though they are very+ifficult to i+entify 8!(

1( Ellumination changes influence generali:ation(

1#( Vie-3generali:ation appears to )e me+iate+ )y temporal association(

1>( otion of faces appears to facilitate su)se/uent recognition(1"( &he visual system starts -ith a ru+imentary preference for face3li4e patterns(

1=( &he visual system progresses from a piecemeal to a holistic strategy over the first

several years of life(

1( &he human visual system appears to +evote speciali:e+ neural resources for face perception(

1!( atency of responses to faces in inferotemporal FE&G corte< is a)out 1 ms,suggesting a largely fee+ for-ar+ computation(

19( .acial i+entity an+ e

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chec43ins, or near o)'ects people are li4ely to stare at Fsee .igure #G( &his type of traps

-oul+ ai+ face recognition soft-are )y helping to capture a straight frontal image -hich

allo- for higher accuracy of the system( Despite their potential )enefit, there appears to )e very little research +one on facetraps(

Figure 3. .igure +epicts increasingly controlle+ environments from left to right 8=(

Some have /uestione+ the legality of face scanning an+ have argue+ that suchsystems -hich are use+ to hunt to criminals in pu)lic places are an invasion of privacy(

.rom a legal perspective, in the Unite+ States, one +oes not have a right to privacy for

things sho-n in pu)lic 8=( .or e

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or lighting( %ecent +eployments of face recognition systems have yiel+e+ poor results

 )ecause faces capture+ in the images vary -i+ely from the +ata)ase images( ne -ay

reme+y to this situation is to use facetraps( .acetraps are cameras -hich are strategically place+ to capture high /uality images of faces( &he goal is to o)tain images -hich are as

close as possi)le to those ta4en in the controlle+ environment(

Figure $. 2i++en cameras -hich loo4 li4e every+ay items -ill )e useful to ensure su)'ects are una-are ofthe cameras Felectrical )o< an+ -all cloc4 -ith hi++en cameras are picture+G(

ur proposal -ill +etermine the effectiveness of +ifferent facetrap setups( Severalfacetrap scenarios -ill )e teste+ in a )usy, pu)lic area( Some set ups -hich -ill )e trie+

are placing cameras facing +oor-ays, near cloc4s, )ehin+ chec43out counters, an+ )ehin+

one -ay mirrors( Et is imperative that hi++en cameras )e use+ so that su)'ects +o notreali:e they are )eing -atche+( &he cameras -ill collect +ata for t-o months( Emage

/uality -ill )e 'u+ge+ on angle, lighting, an+ +istance( .acetraps -hich consistently

yiel+ goo+ results -ill )e note+ as goo+ can+i+ates for actual implementation( .urther

-or4 may involve ne- i+eas for facetrap placement or t-ea4s to previously teste+metho+s(

References

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trou)ling future,K &he .ree i)rary, (

8# ar4 illiams, KBetter .ace3%ecognition Soft-are,K &echnology %evie-, ay #,

(

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