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(PDF) Face Recognition by Independent Component Analysis
Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons.
Independent component analysis - Wikipedia
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 the subcomponents are non-Gaussian signals and that they are statistically independent from each other. ICA is a special case of blind source separation.A common example application is the "cocktail party problem ......
ICA filters for lighting invariant face recognition ...
This paper addresses the problem of face recognition under variation of illumination and poses with large rotation angles using edge information as Independent Component (ICs).
ICA Face Recognition Matlab code - YouTube
A number of face recognition algorithms employ principal component analysis (PCA), which is based on the second-order statistics of the image set, and does not address high-order statistical ...
ICA for dummies – Arnaud Delorme
Independent Component Analysis is a signal processing method to separate independent sources linearly mixed in several sensors. For instance, when recording electroencephalograms (EEG) on the scalp, ICA can separate out artifacts embedded in the data (since they are usually independent of each other).
Face recognition using independent component analysis and ...
Independent component analysis (ICA) (Bell and Sejnowski, 1995) is also a relatively recent technique which has been mainly applied to blind signal separation, though it has been successfully applied to the face recognition problem too. ICA is a feature extraction technique, while SVM are a type of classifiers.
Independent Component Analysis (ICA) - Statistics
Independent Component Analysis (ICA) Independent Component Analysis (.pdf) . Independent component analysis (ICA) is a computational method from statistics and signal processing which is a special case of blind source separation. ICA seeks to separate a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals.
 Independent Component Analysis
10.2 Mutual information and nongaussianity 223 10.3 Mutual information and likelihood 224 10.4 Algorithms for minimization of mutual information 224 10.5 Examples 225 10.6 Concluding remarks and references 225 Problems 227 11 ICA by Tensorial Methods 229 11.1 Definition of cumulant tensor 229 11.2 Tensor eigenvalues give independent components 230...
Ensemble learning for independent component analysis ...
It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased....
Independent components analysis-based nose detection method
The method adopt Independent Components Analysis (ICA) as a subspace classifier to classify the face candidate region to nose or non nose. The ICA basis vectors are estimated by the FastICA algorithm. The training has been done using features of nose appearance and shape characterized by the edge information.
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