ii ABSTRACT This report studies the 1991 Mathew Turk and Alexander Pentland paper: " Face Recognition using Eigenfaces " that appeared in the CVPR proceedings [1]. It has since become an extremely popular approach to face detection. Once implemented, the algorithm proves many of Turk and Pentland's claims of the Eigenface approach to detecting faces with a limited amount of training required. T...

Abstract. Over the years, mathematicians and computer scientists have produced an extensive body of work in the area of facial analysis. Several facial analysis algorithms have been based on mathematical concepts such as the singular value decomposition (SVD). The SVD is generalized in this paper to take advantage of the mirror symmetry that is inherent in faces, thereby developing a new facial...

In this paper, we present a parallel face recognition algorithm based on \eigenfaces", which are known as the signiicant features because they are the eigenvec-tors(principal components) of a set of face images. Our work is composed of two phases: eigenface construction and face recognition. For the construction of eigenfaces, a solution of eigenvalue/eigenvector problem using parallel Jacobi t...

The geometrical character of face space is investigated, and is analyzed within the framework of eigenface decompositions. To deal with the sparse data problem, which occurs when not enough data is present to fill the space, we construct and test a range of probabilistic models. A key tool in this investigation is the decomposition of the eigenfaces representation of face space into odd and eve...

Eigenface approach is one of the simplest and most efficient methods for face recognition. In eigenface approach chosing the threshold, value is a very important factor for performance of face recognition. In addition, the dimensional reduction of face space depends upon number of eigenfaces taken. In this paper, an optimized solution for face recognition is given by taking the optimized value ...

In this paper, we propose a novel technique for expression invariant face recognition, which is different from eigenfaces method from two aspects: the first is that instead of applying Principal Component Analysis (PCA) on the pixel domain to obtain eigenfaces, we train eigenmotion by applying PCA on motion vectors getting from the training face images with expression variations; the second is ...

We tried to develop a real time face detection and recognition system which uses an "appearance-based" approach. For detection purpose we used Viola Jones algorithm. To recognize face we worked with Eigen Faces which is a PCA based algorithm. In a real time to recognize a face we need a data training set. For data training set we took five images of each person and manipulated the Eig...

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approac...

A previous experiment tested subjects’ new/old judgments of previously-studied faces, distractors, and morphs between pairs of studied parents. We examine the extent to which models based on principal component analysis (eigenfaces) can predict human recognition of studied faces and false alarms to the distractors and morphs. We also compare eigenface models to the predictions of previous model...