Face Recognition & Biometric Systems, 2005/2006 Face recognition process

Face Recognition & Biometric Systems, 2005/2006 Plan of the lecture Face recognition process Most useful tools Principal Components Analysis Support Vector Machines Gabor Wavelets Hough Transform Biometric methods

Face Recognition & Biometric Systems, 2005/2006 Face recognition process DetectionNormalisation Feature extraction Feature vectors comparison

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: tools Generalised 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 verification Detection of feature points

Face Recognition & Biometric Systems, 2005/2006 Face tracking Useful in case of video sequences faster than detection smaller precision Tool: Optical flow Tracking 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 transforms Image filtering Histogram 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 faces Geometrical normalisation Lighting compensation Histogram 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 extraction Eigenfaces 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 S1S1 S2S2 SnSn... S K1K1 K2K2 KnKn Two imagesFeature vectorsSimilarities K1K1 K2K2 KnKn...

Face Recognition & Biometric Systems, 2005/2006 Multi-method fusion Average similarity weighted mean SVM with polynomial kernel SVM 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 set Parameters 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 detection Properties: 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 recognition Advantages: 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 recognition Voice recognition Signature analysis

Face Recognition & Biometric Systems, 2005/2006 Thank you for your attention!