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Title: Face recognition using IPCA-ICA algorithm
Authors: Dagher, Issam 
Nachar, Rabih 
Affiliations: Department of Computer Engineering 
Department of Telecommunications and Networking Engineering 
Keywords: Blind source separation
Principal component analysis (PCA)
Independent component analysis (ICA)
Principal non-Gaussian directions
Subjects: Image processing
Issue Date: 2006
Part of: IEEE trasnactions on pattern analysis and machine intelligence
Volume: 28
Issue: 6
Start page: 996
End page: 1000
In this paper, a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA, is introduced. This algorithm computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time transforming these principal components to the independent directions that maximize the non-Gaussianity of the source. Two major techniques are used sequentially in a real-time fashion in order to obtain the most efficient and independent components that describe a whole set of human faces database. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and independent component analysis (ICA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to others.
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Computer Engineering

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