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Face Recognition & Biometric Systems, 2005/2006

Face recognition process


Plan of the lectureFace recognition processMost useful tools Principal Components Analysis Support Vector Machines Gabor Wavelets Hough TransformBiometric methods


Face recognition process

Detection Normalisation

Featureextraction

Feature vectorscomparison

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Face detection: aimsFind a face in the image independent of image size independent of face size for RGB and GS images fast & effective independent from head rotation angleFace location passed to normalisation


Face detection: toolsGeneralised Hough Transform ellipse detectionSupport Vector Machines (SVM) verificationPCA (back projection) verificationGabor Wavelets feature points detectionColour-based face maps


Face detection: algorithm

Detection of ”vertical” ellipses face candidatesDetection of ”horizontal” ellipses eye sockets candidatesInitial normalisation and verificationDetection of feature points


Face tracking

Useful in case of video sequences faster than detection smaller precisionTool: Optical flowTracking of feature points


Normalisation

Input: image from a camera characteristic points locationTarget: generate an image of invariant

parameters eliminate differences within classes


Normalisation: tools

Geometrical transformsImage filteringHistogram modifications histogram fitting to a histogram

of the average face imageLighting compensation


Normalisation: stages

Rotation of non-frontal facesGeometrical normalisationLighting compensationHistogram fitting


Feature extraction

Input: normalised imageTarget: generate a key which describes the

face algorithm of comparing the keys


Feature extraction: tools

Principal Component Analysis Linear Discriminant Analysis Local PCA Bayesian MatchingGabor Wavelets


Feature vectors comparison

Coherent with feature extractionEigenfaces geometric distances SVMDual Eigenfaces image difference classifiedElastic Bunch Graph Matching correlation based


Multi-method fusion

Many feature extraction methods

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Two images Feature vectors Similarities

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Multi-method fusion

Average similarity weighted meanSVM with polynomial kernelSVM for finding optimal weights


Tools: PCAApplications: feature extraction – the Eigenfaces

method detection (back projection) Dual Eigenfaces

Stages: training feature extraction feature vectors comparison


Tools: SVM

Applications: face detection – verification feature vectors comparison detection of lighting direction estimation of head rotation angle multi-method fusion image quality assessment


Tools: SVMStages: training classification

Main idea: data mapped into higher dimension to

achieve linear separability mapping performed by application of

kernelsProblems with training setParameters must be selected properly


Tools: Gabor WaveletsApplications: feature extraction (EBGM method) feature points detection face tracking (the detected points are

tracked)Properties: local frequency analysis set of various wavelets prepared comparison: correlation with displacement

estimation


Tools: GHT

Useful for face detectionProperties: directional image generated (set of

segments) probable ellipse centre for every

segment (based on templates) accumulation of the results for all

the segments in the image


Biometric methodsTypes of the methods: static dynamic (behavioural)

Requirements: universality distinctiveness permanence collectability performance acceptability circumvention


Face recognitionAdvantages: low invasiveness high speed identification support systemDrawbacks: relatively low effectiveness changeability of a face face is not always visible


Fingerprint recognition

Advantages: high effectiveness useful for forensic applicationsDisadvantages: long acquisition time low acceptability


Iris recognition

Advantages: high distinctiveness universalityDrawbacks: high quality image required low permanence in young age


Behavioural methods

Gait recognitionVoice recognitionSignature analysis


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