Face recognition process
26
Face Recognition & Biometric Systems, 2 005/2006 Face recognition process
description
Face recognition process. Plan of the lecture. Face recognition process Most useful tools Principal Components Analysis Support Vector Machines Gabor Wavelets Hough Transform Biometric methods. Face recognition process. Detection. Normalisation. Feature vectors comparison. Feature - PowerPoint PPT Presentation
Transcript of Face recognition process
Face Recognition & Biometric Systems, 2005/2006
Face recognition process
Face Recognition & Biometric Systems, 2005/2006
Plan of the lectureFace recognition processMost useful tools Principal Components Analysis Support Vector Machines Gabor Wavelets Hough TransformBiometric methods
Face Recognition & Biometric Systems, 2005/2006
Face recognition process
Detection Normalisation
Featureextraction
Feature vectorscomparison
Face Recognition & Biometric Systems, 2005/2006
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 Recognition & Biometric Systems, 2005/2006
Face detection: toolsGeneralised Hough Transform ellipse detectionSupport Vector Machines (SVM) verificationPCA (back projection) verificationGabor Wavelets feature points detectionColour-based face maps
Face Recognition & Biometric Systems, 2005/2006
Face detection: algorithm
Detection of ”vertical” ellipses face candidatesDetection of ”horizontal” ellipses eye sockets candidatesInitial normalisation and verificationDetection of feature points
Face Recognition & Biometric Systems, 2005/2006
Face tracking
Useful in case of video sequences faster than detection smaller precisionTool: Optical flowTracking of feature points
Face Recognition & Biometric Systems, 2005/2006
Normalisation
Input: image from a camera characteristic points locationTarget: generate an image of invariant
parameters eliminate differences within classes
Face Recognition & Biometric Systems, 2005/2006
Normalisation: tools
Geometrical transformsImage filteringHistogram modifications histogram fitting to a histogram
of the average face imageLighting compensation
Face Recognition & Biometric Systems, 2005/2006
Normalisation: stages
Rotation of non-frontal facesGeometrical normalisationLighting compensationHistogram fitting
Face Recognition & Biometric Systems, 2005/2006
Feature extraction
Input: normalised imageTarget: generate a key which describes the
face algorithm of comparing the keys
Face Recognition & Biometric Systems, 2005/2006
Feature extraction: tools
Principal Component Analysis Linear Discriminant Analysis Local PCA Bayesian MatchingGabor Wavelets
Face Recognition & Biometric Systems, 2005/2006
Feature vectors comparison
Coherent with feature extractionEigenfaces geometric distances SVMDual Eigenfaces image difference classifiedElastic Bunch Graph Matching correlation based
Face Recognition & Biometric Systems, 2005/2006
Multi-method fusion
Many feature extraction methods
S1
S2
Sn
... S
K1
K2
Kn
...
Two images Feature vectors Similarities
K1
K2
Kn
...
Face Recognition & Biometric Systems, 2005/2006
Multi-method fusion
Average similarity weighted meanSVM with polynomial kernelSVM for finding optimal weights
Face Recognition & Biometric Systems, 2005/2006
Tools: PCAApplications: feature extraction – the Eigenfaces
method detection (back projection) Dual Eigenfaces
Stages: training feature extraction feature vectors comparison
Face Recognition & Biometric Systems, 2005/2006
Tools: SVM
Applications: face detection – verification feature vectors comparison detection of lighting direction estimation of head rotation angle multi-method fusion image quality assessment
Face Recognition & Biometric Systems, 2005/2006
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
Face Recognition & Biometric Systems, 2005/2006
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
Face Recognition & Biometric Systems, 2005/2006
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
Face Recognition & Biometric Systems, 2005/2006
Biometric methodsTypes of the methods: static dynamic (behavioural)
Requirements: universality distinctiveness permanence collectability performance acceptability circumvention
Face Recognition & Biometric Systems, 2005/2006
Face recognitionAdvantages: low invasiveness high speed identification support systemDrawbacks: relatively low effectiveness changeability of a face face is not always visible
Face Recognition & Biometric Systems, 2005/2006
Fingerprint recognition
Advantages: high effectiveness useful for forensic applicationsDisadvantages: long acquisition time low acceptability
Face Recognition & Biometric Systems, 2005/2006
Iris recognition
Advantages: high distinctiveness universalityDrawbacks: high quality image required low permanence in young age
Face Recognition & Biometric Systems, 2005/2006
Behavioural methods
Gait recognitionVoice recognitionSignature analysis
Face Recognition & Biometric Systems, 2005/2006
Thank you for your attention!
