The human visual system can implicitly extract a prototype of encountered visual objects (Posner & Keele, 1968). While learning a prototype provides an efficient way of encoding objects at the category level, discrimination among individual objects requires encoding of variations among them as well. Here we show that in addition to the prototype, human adults also implicitly learn the feature c...

Eigenface or Principal Component Analysis (PCA) methods have demonstrated their success in face recognition, detection, and tracking. The representation in PCA is based on the second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics (HOS) have been used as a more in...

Face recognition is one among the several techniques for identification and verification of an individual. The approach in the present work transforms face images into a small set of characteristic feature images called eigenfaces, which are the principal components of the initial training set of face images. Recognition is performed by projecting a new image into subspace spanned by eigenfaces...

In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and o...

Face recognition is a very challenging issue and has attracted much attention over the past decades. This paper makes a new attempt to face recognition based on 3D point clouds by constructing 3D eigenfaces. First, a 3D mesh model is built to represent the face shape provided by the point cloud. Then, the principle component analysis (PCA) is used to construct the 3D eigenfaces, which describe ...

We present an approach t o the detection and identification of human faces and describe a working, near-real-time face recognition system which tracks a subject’s head and then recognizes the person by comparing characteristics of the face to those of known individuals. Our approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are are...

This paper is an experimental study on the robustness of the eigenfaces method for face recognition. To build a face recognition system, especially in an unconstrained surveillance system where a clear, direct, and normalized view of the face cannot be assumed, one needs to implement several image preprocessing steps like segmentation, deskewing, zooming, rotation, warping, etc., before process...

This project is to implement a 2D face recognition algorithm proposed in [2], which models the density of intrapersonal and extrapersonal face space separately with a single Gaussian for each, and thus uses Bayesian theory to do classification. It includes both maximum a posteriori (MAP) and maximum likelihood (ML) decision. Besides, we will try two improvements: one is to use Gaussian Mixture ...