Publication

Independent Component Analysis and Support Vector Machine for Face Feature Extraction

Jean-Philippe Thiran, Vlad Popovici
2003
Conference Papers

Abstract

We propose Independent Component Analysis representation and Support Vector Machine classification to extract facial features in a face detection/localization context. The goal is to find a better space where project the data in order to build ten different face-feature classifiers that are robust to illumination variations and bad environment conditions. The method was tested on the BANCA database, in different scenarios: controlled conditions, degraded conditions and adverse conditions.

Official source

https://infoscience.epfl.ch/entities/publication/8c57c053-b59a-4153-84d1-a9e45e90dcf0

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Jean-Philippe Thiran, Vlad Popovici
2003
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/8c57c053-b59a-4153-84d1-a9e45e90dcf0

About this result

Related concepts (27)

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.

Independent component analysis

In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.

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