Publication

Face Authentication using Client-specific Matching Pursuit

Jean-Philippe Thiran, Pierre Vandergheynst
2004
Reports

Abstract

In this paper, we address the problem of finding image decompositions that allow good compression performance, and that are also efficient for face authentication. We propose to decompose the face image using Matching Pursuit and to perform the face authentication in the compressed domain using a MLP (Multi-Layer Perceptron) classifier. We provide experimental results and comparisons with PCA and LDA systems on the multi-modal benchmark database BANCA using its associated protocol.

Official source

https://infoscience.epfl.ch/entities/publication/96b68826-b425-4eb0-82e4-a24b8085d33b

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Jean-Philippe Thiran, Pierre Vandergheynst
2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/96b68826-b425-4eb0-82e4-a24b8085d33b

About this result

Related concepts (30)

Matching pursuit

Matching pursuit (MP) is a sparse approximation algorithm which finds the "best matching" projections of multidimensional data onto the span of an over-complete (i.e., redundant) dictionary . The basic idea is to approximately represent a signal from Hilbert space as a weighted sum of finitely many functions (called atoms) taken from . An approximation with atoms has the form where is the th column of the matrix and is the scalar weighting factor (amplitude) for the atom . Normally, not every atom in will be used in this sum.

Basis pursuit

Basis pursuit is the mathematical optimization problem of the form where x is a N-dimensional solution vector (signal), y is a M-dimensional vector of observations (measurements), A is a M × N transform matrix (usually measurement matrix) and M < N. It is usually applied in cases where there is an underdetermined system of linear equations y = Ax that must be exactly satisfied, and the sparsest solution in the L1 sense is desired. When it is desirable to trade off exact equality of Ax and y in exchange for a sparser x, basis pursuit denoising is preferred.

Sparse approximation

Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use in , signal processing, machine learning, medical imaging, and more. Consider a linear system of equations , where is an underdetermined matrix and . The matrix (typically assumed to be full-rank) is referred to as the dictionary, and is a signal of interest.

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