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Face Recognition & Biometric Systems, 2005/2006
Face recognition process
Face Recognition & Biometric Systems, 2005/2006
Plan of the lecture
Face recognition processMost useful tools Principal Components Analysis Support Vector Machines Gabor Wavelets Hough Transform
Biometric methods
Face Recognition & Biometric Systems, 2005/2006
Face recognition process
Detection Normalisation
Featureextraction
Feature vectorscomparison
Face Recognition & Biometric Systems, 2005/2006
Face detection: aims
Find a face in the image independent of image size independent of face size for RGB and GS images fast & effective independent from head rotation angle
Face location passed to normalisation
Face Recognition & Biometric Systems, 2005/2006
Face detection: toolsGeneralised Hough Transform ellipse detection
Support Vector Machines (SVM) verification
PCA (back projection) verification
Gabor Wavelets feature points detection
Colour-based face maps
Face Recognition & Biometric Systems, 2005/2006
Face detection: algorithm
Detection of ”vertical” ellipses face candidates
Detection of ”horizontal” ellipses eye sockets candidates
Initial normalisation and verificationDetection of feature points
Face Recognition & Biometric Systems, 2005/2006
Face tracking
Useful in case of video sequences faster than detection smaller precision
Tool: Optical flowTracking of feature points
Face Recognition & Biometric Systems, 2005/2006
Normalisation
Input: image from a camera characteristic points location
Target: 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 image
Lighting 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 image
Target: 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 Matching
Gabor Wavelets
Face Recognition & Biometric Systems, 2005/2006
Feature vectors comparison
Coherent with feature extractionEigenfaces geometric distances SVM
Dual Eigenfaces image difference classified
Elastic Bunch Graph Matching correlation based
Face Recognition & Biometric Systems, 2005/2006
Multi-method fusion
Many feature extraction methods
S1
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Two images Feature vectors Similarities
K1
K2
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Face Recognition & Biometric Systems, 2005/2006
Multi-method fusion
Average similarity weighted mean
SVM with polynomial kernelSVM for finding optimal weights
Face Recognition & Biometric Systems, 2005/2006
Tools: PCA
Applications: 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: SVM
Stages: training classification
Main idea: data mapped into higher dimension to
achieve linear separability mapping performed by application of
kernels
Problems with training setParameters must be selected properly
Face Recognition & Biometric Systems, 2005/2006
Tools: Gabor Wavelets
Applications: 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 methods
Types 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 system
Drawbacks: 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 applications
Disadvantages: long acquisition time low acceptability
Face Recognition & Biometric Systems, 2005/2006
Iris recognition
Advantages: high distinctiveness universality
Drawbacks: 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
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