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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 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.
Independent Component Analysis - an overview ...
jICA is an extension of the popular independent component analysis (ICA). Briefly, ICA is a technique for revealing hidden factors that underlie a set of observable data. ICA has been widely used to solve blind source separation problems (Fig. 16.3); these include, for example, the problem of deriving brain waves recorded using multiple sensors and the problem of removing interfering radio ...
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 ...
Face Recognition Based on ICA Combined with FLD
Abstract. Recently in face recognition, as opposed to our expectation, the performance of an ICA (Independent Component Analysis) method combined with LDA (Linear Discriminant Analysis) was reported as lower than an ICA only based method.
(PDF) Multilinear independent component analysis | Demetri ...
In particular, the linear, After reviewing the mathematical foundations of our appearance-based face recognition method known as Eigen- work in the next section, we introduce our multilinear ICA faces [9] is founded on the principal components analysis algorithm in Section 3 and develop the associated recogni- (PCA) of facial image ensembles [7]....
Research - M. Alex O. Vasilescu - MIT Media Lab
In the context of facial image ensembles, we demonstrate that the statistical regularities learned by MICA capture information that improves automatic face recognition. "Multilinear (Tensor) ICA and Dimensionality Reduction", M.A.O. Vasilescu, D. Terzopoulos, Proc. 7th International Conference on Independent Component Analysis and Signal ......
icalab ICA : Independent component analysis, based - CodeBus
icalab ICA : Independent component analysis, based on MATLAB. FastICA_2.4] - based on independent component analysis [independentcomponentanalysis(maltabcode)used] - independent component analysis (maltab c[] - fast algorithm, the test after test, we[Classification-MatLab-Toolbox] - pattern recognition Matlab toolbox, incl[] - ICA achieving Matlab feature extraction...
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).
Independent Component Analysis - Papers With Code
Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent ...
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