Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces) that does not affects the recognition p...

The principal component analysis (PCA) faces the problem of high computation complexity and inaccurate estimated covariance matrix from training face images for face recognition. The expressive feature face recognition algorithm (EFFRA) is proposed. In EFFRA, the subspace basic vector extracted by PCA is substituted by the right singular vectors of training images, so that the transformation fr...

A common pre-processing step is to project the data into a lower-dimensional subspace, before applying k-NN estimator. One example of this is the Eigenfaces algorithm for face recognition. PCA is applied on a database of face images (aligned, of fixed dimension) to get a principal subspace (of much lower dimensionality than the original, which is the number of pixels in the image). For some fix...

We have devised a new class of fast adaptation techniques for speech recognition, based on prior knowledge of speaker variation. To obtain this prior knowledge, one applies Principal Component Analysis (PCA) [9] or a similar technique to a training set of T vectors of dimension D derived from T speaker-dependent (SD) models. This offline step yields T basis vectors, which we call “eigenvoices” ...

We have proposed a new feature extraction method and a new feature fusion strategy based on generalized canonical correlation analysis (GCCA). The proposed method and strategy have been applied to facial feature extraction and recognition. Compared with the face feature extracted by canonical correlation analysis (CCA), as in a process of GCCA, it contains the class information of the training ...

Face recognition has been widely explored in the past years. A lot of techniques have been applied in various applications. Robustness and reliability have become more and more important for these applications especially in security systems. In this paper, a variety of approaches for face recognition are reviewed first. These approaches are classified according to three basic tasks: face repres...

Human face conveys to other human beings, and potentially to computers, much information such as identity, emotional states, intentions, age and attractiveness. Among this information there are kinship clues. Face kinship signals, as well as the human capabilities of capturing them, are studied by psychologist and sociologists. In this paper we present a new research aimed at analyzing, with im...

Over the last years we experienced a revolution in the creation and dissemination of media content such as videos and photos. However, this information is not cataloged and there is no automatic system that indexes movies according to its content. To solve this problem we propose FaceID@home. This system will automatically detect and recognize faces using the Eigenfaces algorithm. To tackle the...

Traditional subspace methods for face recognition compute a measure of similarity between images after projecting them onto a xed linear subspace that is spanned by some principal component vectors (a.k.a. \eigenfaces") of a training set of images. By supposing a parametric Gaussian distribution over the subspace and a symmetric Gaussian noise model for the image given a point in the subspace, ...

Multilinear algebra, the algebra of higher-order tensors, offers a potent mathematical framework for analyzing ensembles of images resulting from the interaction of any number of underlying factors. We present a dimensionality reduction algorithm that enables subspace analysis within the multilinear framework. This N -mode orthogonal iteration algorithm is based on a tensor decomposition known ...