| In this study, a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA, is introduced. 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. The first technique is called incremental principal component analysis (IPCA) which is an incremental version of the popular unsupervised principal component technique. The traditional PCA algorithm computes eigenvectors and eigenvalues for a sample covariance matrix derived from a well-known given image data matrix, by solving an eigenvalue system problem. The second technique is called independent component analysis (ICA). It is used to estimate the independent characterization of human faces. |
