A ReviewofFaceRecognition Techniques forIn-Camera Applications (2007)

Thispaperpresents anoverview ofa numberof Takingadvantage ofrecent advances inembedded face classical facerecognition techniques, including anassessment of detection (2)wepropose toanalyze theuseofembedded face their suitability forapplications inreal-time embedded systems recognition algorithms incamera applications. suchasdigital cameras, anda generalized discussion ofthe Special issues havetobeaddressed forthealgorithms in training requirements fordifferent facerecognition techniques. order tomakepossible their implementation inhardware. Most Also, anintroduction tofacerecognition approaches based on hidden MarkovModels (HMM)ismade,andtheir advantages oftheseissues regard thecomplexity ofthealgorithms, their andbenefits areshown, particularly their ability toprovidespeed andtheir special requirements regarding needed memory. acceptable levels ofaccurate recognition forunseencollections of II FACEREGIONPREPROCESSING ALGORITHMS facial images. Finally, adetailed description/implementation ofa working HMM testbedisdone, together withapresentation of somepreliminary results. A. FaceDetection Theproblem ofdetecting faces inimage iswellknown(15, 16)andwill notbediscussed indetail inthis paper. We have I. INTRODUCTION evaluated anumberofautomatic facedetection techniques and Facerecognition remains oneofthemostimportant areas in thebestsuccess rates, inourexperience, areobtained froma computer vision withasuccessful history, andstill captures thevariant ofthealgorithm described in(16)butwithsignificant attention ofmanyresearchers frombothacademic andindustrial customizations toutilize thehardware imageprocessing environments. Oneofthemainreasons forthis interest isthecapabilities ofadigital camera. large areaofcommercial applications wherefacerecognition Itisworthwhile remarking that900+ofthetimeinitially systems couldbeusedstarting withsmall, basicloginrequired byouralgorithm wasspent intheface detection phase. applications andfinishing withhighsecurity access control, Fortunately therecent availability ofhighly optimized in- secure biometric basedtransactions orsecurity surveillance camera face detection systems hassolved this bottleneck. Thus which requires averyhigh accuracy. practical in-camera facerecognition systems arelikely to Digital cameras continue togainterritory inthefight withbecome areality inthenext18-24 months. filmphoto cameras andareoneoftheCE industry's recent success stories. Howeverasusers switch fromconventional to B FaceRegion Normalization digital photography they find themselves withrapidly growingAfter afaceregion isdetected itisgenerally necessary totry collections ofdigital images. Fewconsumers havethetimeand andalign and/or resize theregion sothat itcanbesubsequently personal discipline tomanually catalog andorganize theseanalyzed bystandard facerecognition tools. Thesystem growing personal image collections. described inthis paper hasbeenrestricted to2-Dnormalization We showed in(1)howwecanhelp users toautomatic sorttechniques. Thisismainly toreducethecomputational their digital collection ofimages using thepeople intheimagesrequirements ofthenormalization algorithms with aviewtoan aspatterns. Forthiswe havetodetect andclassify theeventual in-camera implementation. Notethatwe have individuals, whichinvolve algorithms forfacedetection and achieved useful preliminary results on3Dnormalization using recognition. active appearance model(AAM)techniques (17) butthe ForPC applications there aremanyalternatives forfaceeffectiveness andaccuracy ofthese isstill somewhat dependent detection andrecognition, butforembedded applications theseontheprototype models employed. problems became morechallenging duetothelack ofresources. We also havetoconsider thefact that we areworking with III.FACERECOGNITIoN ALGORITHMS consumer images withlarge variations that affect theaccuracyThegoalofoursystem isthedetection andrecognition of ofclassical face recognition algorithms.