08-Biometrics-Lecture-8-Part3-2008-11-10

08-Biometrics-Lecture-8-Part3-2008-11-10 - Master SC...

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1 Biometrics http://scgwww.epfl.ch/courses Master SC – Information and Communication Security Dr. Andrzej Drygajlo Speech Processing and Biometrics Group Signal Processing Institute Ecole Polytechnique Fédérale de Lausanne (EPFL) Center for Interdisciplinary Studies in Information Security (ISIS)
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2 Face Recognition Face detection Face tracking Face recognition (appearance-based) Local features ± DCT-based methods Global features (holistic approach) ± Principal Component Analysis (PCA) ± Linear Discriminant Analysis (LDA) Performance evaluation Advantages and disadvantages
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3 Principal Component Analysis (PCA) Principal component analysis (PCA), or Karhunen-Loeve transformation , is a data-representation method that finds an alternative set of parameters for a set of raw data (or features) such that most of the variability in the data is compressed down to the first few parameters The transformed PCA parameters are orthogonal The PCA, diagonalizes the covariance matrix, and the resulting diagonal elements are the variances of the transformed PCA parameters
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4 PCA Advantages It completely decorrelates any data in the transform domain It packs the most energy (variance) in the fewest number of transform coefficients It minimizes the MSE (mean square error) between the reconstructed and original data for any specified data compression It minimizes the total entropy of the data Disadvantages There is not fast algorithm for its implementation The PCA is not a fixed transform , but has to be generated for each type of data statistic There is considerable computational effort involved in generation of eigenvalues and eigenvectors of the covariance matrices
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5 PCA
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6 PCA The covariance (scatter) matrix of the data , which encodes the variance and covariance of the data, is used in PCA to find the optimal rotation of the parameter space PCA finds the eigenvectors and eigenvalues of the covariance matrix.
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This note was uploaded on 06/25/2009 for the course MATH MAT 400 taught by Professor Jamespotvein during the Fall '08 term at University of Toronto- Toronto.

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08-Biometrics-Lecture-8-Part3-2008-11-10 - Master SC...

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