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FACE RECOGNITION

FACE RECOGNITION. AUTHOR: Łukasz Przywarty - 171018. Table of contents. Introduction Recognition process Face detection Feature extraction Face recognition Application example Summary Literature. Face recognition – 2/18. Introduction. Why ?. Face recognition – 3 /18.

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FACE RECOGNITION

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  1. FACE RECOGNITION AUTHOR: Łukasz Przywarty - 171018

  2. Table of contents • Introduction • Recognitionprocess • Face detection • Featureextraction • Face recognition • Applicationexample • Summary • Literature Face recognition– 2/18

  3. Introduction Why? Face recognition– 3/18

  4. Introduction • Since when? • 1960’s – semi-automatedsystem: requiredthe administrator to locateface coordinates; computer usedthis for recognition • 1970’s – Goldstein, Harmon, Lesk: vectorcontaining 21 featurese.geyebrowweight, noselength as thebasis to recognizefaces (patternclassification) • 1986– Kirby, Sirovich: methodsbased on PCA (Principal ComponentAnalysis); goal: representimageinlowerdimensionwithoutlosing much information; dominant approachinfollowingyears Face recognition– 4/18

  5. Introduction • Problems? • Posevariations • Observationconditions (angle, light, shadows, reflections etc.) • Ageing • Facialexpression • Facialocculsion: make-up, hairstyle, accesories Face recognition– 5/18

  6. Recognitionprocess • How to do it? • How to detect face? • Detectiondepending on scenario: • Controlled environment – simpleedgedetectiontechniques • Colorimages – skin colorscan be used to findfaces • Imagesinmotion – e.gblinkdetection Face recognition– 6/18

  7. Recognitionprocess • Howto detect face? • Detectionmethods: • Knowledge –basedmethods : • theytry to captureourknowledge of faces and translatetheminto set of rules (face hastwosymmetriceyes, theeyeareaisdarkerthanthecheeks etc), • facialfeaturescould be thedistancebetweeneyesorcolorintensitydifference. • Feature-invariantmethods: • algorithmsthattry to findinvariantfeatures of a face despiteit’sangleorposition Face recognition– 7/18

  8. Recognitionprocess • Howto detect face? • for example: algorithmsthatdetectface-liketexturesorthecolor of human skin. • Templatematching • try to define face as a function and find a standard template of allthefaces, • templatecolud be: face contour, relationbetween face regions interms of brightness and darkness, • limited to facesthatarefrontal. • Appearance-basedmethods • statisticalanalysis. Face recognition– 8/18

  9. Recognitionprocess • Howto standarizeimage? • Histogram modification • Imagefiltration • Geometricaltransformation • Rotate • Scale • Move • Resize • Desaturationorcolormodification Face recognition– 9/18

  10. Division of face recognition systems • Feature-basedapproach • First, most intuitive idea • First step: localization of points on face images: • eyescentrepoints • nosestart-endpointsetc. • Next step: measuring: • face, nosewidth, height etc. • distancesbetweeneyescentres, nose and eyes etc. • Problems • Accuratepointslocalization Face recognition– 10/18

  11. Divisionof face recognition systems • Feature-basedapproach • Usedmethods: • GeometricMatching • Bunch GraphMatching • HiddenMarkov Model Techniques Face recognition– 11/18

  12. Divisionof face recognition systems • Holisticapproach • Whole face analysis • Methodsbased on: • Correlation: • simplemethodoperating on inputimagepixels, • directcomparision to a patternindatabase, • worksifimagesweretakeninalmostthe same conditions • PCA (Principal ComponentAnalysis ) and eigenfacesconcept: • featuredimensionreduction (convertstwodimensionalvectorsintoone dimensionalvector) • extractsthefeatures of face whichvarythemost, Face recognition– 12/18

  13. Divisionof face recognition systems • Holisticapproach • problem: imagemust be thesame size and normalized; poseand illuminationvariationin not acceptable, • rate od recognition: 95% • LDA (LinearDiscriminateAnalysis) and Fisherfaceconcept Face recognition– 13/18

  14. Divisionof face recognition systems • Hybridapproach • Bothlocalfeature and whole face • Methodsbased on: • AAM (ActiveAppearance Model) • integratedstatistical model whichcombines a model of shapevariation and apperancewithnewimage, • builtduringa trainingphase, • comparesbothwhole face shape and pixelsbrightnessaroundfeature. Face recognition– 14/18

  15. Applicationexample • Picasa 3.5 • Staticimages • LuxandFaceSDK • 66 featurepoints • -30-30 degreesheadrotationsupport • 49 700 faces per second • Verilook 5.1 • Multifaceprocessing • Live face detection • Tolerance to face posture (near 360 degrees) • 44 000 faces per second • Multiplesamples of same face Face recognition– 15/18

  16. Finalword • Summary? • Despite of 40 years development stillunreliable • 12% of biometrictechnologies (2nd place, afterprint) • Loweffectivenessin pilot projects (UK: Newham, USA: Tampa) • Failedtrialinairports Face recognition– 16/18

  17. Literature • E.Bagherian, R.Wirza O.K. Rahmat. „Facial feature extraction for face recognition: a review” • C. Iancu, P. Corcoran, G. Costache . „A review of face recognitiontechniques for in-cameraapplications” • M. Smiatacz, W. Malina. „Automatic face recognition – methods, problems and applications” • K. Ślot. „Rozpoznawanie biometryczne” • K. Ślot. „Wybrane zagadnienia biometrii” Face recognition– 17/18

  18. FACE RECOGNITION Thankyou for yourattention!

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