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 Face Modeling by Information Maximization
[42] C. Liu and H. Wechsler. Comparative assessment of independent component analysis (ica) for face recognition. In International conference on audio and video based biometric person authentication, 1999. [43] Q. Liu, J. Cheng, H. Lu, and S. Ma. Modeling face appearance with nonlinear independent component analysis....
Randomized Independent Component Analysis | DeepAI
Independent component analysis (ICA) is a well-established problem in unsupervised learning and signal processing, with numerous applications including blind source separation, face recognition, and stock price prediction. onsider the following scenario.A couple of speakers are located in a room. Each of them plays a different sound .
Independent Component Analysis - Cambridge Core
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.
Multilinear Independent Components Analysis
Multilinear Independent Components Analysis M. Alex O. Vasilescu1,2 and Demetri Terzopoulos2,1 1Department of Computer Science, University of Toronto, Toronto ON M5S 3G4, Canada 2Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA Abstract IndependentComponentsAnalysis(ICA)maximizesthesta-tistical independence of the representational components of...
Face recognition: A literature survey: ACM Computing ...
Discriminant analysis of principal components for face recognition. In Proceedings, International Conference on Automatic Face and Gesture Recognition. 336--341.]] Google Scholar Digital Library; Zhao, W., Chellappa, R., and Phillips, P. J. 1999. Subspace linear discriminant analysis for face recognition....
Independent component analysis: an introduction: Trends in ...
Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides ...
ISSN : 2454-9150 SVM and ANN-based Comparison of Face ...
Abstract— Face recognition is a very important component of human intelligence. For individual identity faces are rich in information. Since last few years, face recognition have been most important and successful applications of machine learning and computer security. The major method for face recognition consists of two steps....
Applying Artificial Neural Networks for Face Recognition
The model is assumed to have The strongest edge, correlation, and statistical model of profile. ... The Association of Geometric Feature-Based Method and Independent Component Analysis in Facial Feature Extraction ... “Face recognition by independent component analysis,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1450–1464 ......
(PDF) REAL-TIME FACE RECOGNITION USING EIGENFACES | IJESRT ...
[10] Sequential row–column independent component analysis for face recognitionNeurocomputing,by (Quanxue Gao, Lei Zhang, David Zhang) [11] C. Liu, H. Wechsler, Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition, Proc. of the Second International Conference on Audio- and Video-based Biometric Person ......
Region-Based Representations for Face Recognition
accurately recognize the stimuli. Given that this does not occur, we can infer that fine edge information is not a critical prerequisite for face recognition. Given the limitations of pixel and edge-based primitives, we propose the use of novel image mea-surements that implement a region-based representational strategy.
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