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Viewing 21-30 of 50 total results
A Robust Method for Nose Detection under Various Conditions
It depends on the local appearance and shape of nose region characterized by edge information. Independent Components Analysis (ICA) is used to learn the appearance of nose. We show experimentally that using edge information for characterizing appearance and shape outperforms using intensity information....
 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 Component Analysis edited by Stephen Roberts
Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models.
Face Recognition Using Independent Component Analysis and ...
Support Vector Machine Face Recognition Face Image Independent Component Analysis Face Database These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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.
 Face Recognition: From Traditional to Deep Learning Methods
Fig. 2: Face recognition building blocks. face recognition research, as CNNs are being used to solve many other computer vision tasks, such as object detection and recognition, segmentation, optical character recognition, facial expression analysis, age estimation, etc. Face recognition systems are usually composed of the following building blocks:...
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 Facial Feature Extraction by Kernel Independent Component ...
representation method for face recognition. The proposed method, referred as Kernel ICA, combines the strengths of the Kernel and Independent Component Analysis (ICA) approaches. For performing Kernel ICA, we employ an algorithm developed by F. R. Bach and M. I. Jordan. This algorithm has proven successful for separating randomly
3D reconstruction and face recognition using kernel-based ...
Research highlights We propose an improved kernel-independent component analysis method to reconstruct 3D human faces. A three-layer feed-forward neural network trained by a back-propagation algorithm is used to realize a classifier. The experimental results demonstrate that the proposed method is efficient in reconstruction and face recognition applications....
 Independent Component Analysis: Algorithms and Applications
The statistical model in Eq. 4 is called independent component analysis, or ICA model. The ICA model is a generative model, which means that it describes how the observed data are generated by a process of mixing the components si. The independent components are latent variables, meaning that they cannot be directly observed.
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