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Face recognition process

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

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Face recognition process

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

  2. 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

  3. Face recognition process Detection Normalisation Feature vectors comparison Feature extraction Face Recognition & Biometric Systems, 2005/2006

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. Normalisation: stages • Rotation of non-frontal faces • Geometrical normalisation • Lighting compensation • Histogram fitting Face Recognition & Biometric Systems, 2005/2006

  11. Feature extraction • Input: • normalised image • Target: • generate a key which describes the face • algorithm of comparing the keys Face Recognition & Biometric Systems, 2005/2006

  12. Feature extraction: tools • Principal Component Analysis • Linear Discriminant Analysis • Local PCA • Bayesian Matching • Gabor Wavelets Face Recognition & Biometric Systems, 2005/2006

  13. 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

  14. Multi-method fusion • Many feature extraction methods K1 K1 S1 K2 K2 S2 S ... ... ... Kn Kn Sn Two images Feature vectors Similarities Face Recognition & Biometric Systems, 2005/2006

  15. Multi-method fusion • Average similarity • weighted mean • SVM with polynomial kernel • SVM for finding optimal weights Face Recognition & Biometric Systems, 2005/2006

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. Biometric methods • Types of the methods: • static • dynamic (behavioural) • Requirements: • universality • distinctiveness • permanence • collectability • performance • acceptability • circumvention Face Recognition & Biometric Systems, 2005/2006

  22. 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

  23. Fingerprint recognition • Advantages: • high effectiveness • useful for forensic applications • Disadvantages: • long acquisition time • low acceptability Face Recognition & Biometric Systems, 2005/2006

  24. Iris recognition • Advantages: • high distinctiveness • universality • Drawbacks: • high quality image required • low permanence in young age Face Recognition & Biometric Systems, 2005/2006

  25. Behavioural methods • Gait recognition • Voice recognition • Signature analysis Face Recognition & Biometric Systems, 2005/2006

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

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