Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

A survey of face detection, extraction and recognition

Abstract: The goal of this paper is to present a critical survey of existing lite- ratures on human face recognition over the last 4-5 years Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001 While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts As the number of proposed techniques increases, survey and evaluation becomes important

Analysis and comparison of eigenspace-based face recognition approaches

Abstract: Different eigenspace-based approaches have been proposed for the recognition of faces They differ mostly in the kind of projection method being used and in the similarity matching criterion employed The aim of this paper is to present a comparative study between some of these different approaches This study considers theoretical aspects as well as experiments performed using a face database with a few number of classes (Yale) and also with a large number of classes (FERET)

Face recognition using Principal Component Analysis method

Abstract: This paper mainly addresses the building of face recognition system by using Principal Component Analysis (PCA). PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. These eigenvectors are obtained from covariance matrix of a training image set. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a test image onto the subspace spanned by the eigenfaces and then classification is done by measuring minimum Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system. In this thesis, we used a training database of students of Electronics and Telecommunication Engineering department, Batch-2007, Rajshahi University of Engineering and Technology, Bangladesh.

Evolving faces from principal components.

Abstract: A system that uses an underlying genetic algorithm to evolve faces in response to user selection is described. The descriptions of faces used by the system are derived from a statistical analysis of a set of faces. The faces used for generation are transformed to an average shape by defining locations around each face and morphing. The shape-free images and shape vectors are then separately subjected to principal components analysis. Novel faces are generated by recombining the image components (eigenfaces) and then morphing their shape according to the principal components of the shape vectors (eigenshapes). The prototype system indicates that such a statistical analysis of a set of faces can produce plausible, randomly generated photographic images.