Eigenface

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

Eigenhill vs. eigenface and eigenedge

Abstract: In this study, we present a new approach to overcome the problems in face recognition associated with illumination changes by utilizing the edge images rather than intensity values. However, using edges directly has its problems. To combine the advantages of algorithms based on shading and edges while overcoming their drawbacks, we introduced "hills" which are obtained by covering edges with a membrane. Each hill image is then described as a combination of most descriptive eigenvectors, called "eigenhills", spanning hills space. We compare the recognition performances of eigenface, eigenedge and eigenhills methods by considering illumination and orientation changes on Purdue A&R face database and showed experimentally that our approach has the best recognition performance.

Face detection and recognition using PCA

Abstract: In this paper we developed a computer system that can locate and track a subject's head in a complex background 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 color and motion Information and PCA (principal component analysis). Our approach treats the face recognition problem as a two-dimensional (2-D) problem rather than three-dimensional geometry. So, the problem is easier to treat. The system functions by two steps, first, extracting face image in a complex background using difference image and color model, and second, projecting pre-extracted face images onto a feature space that represents the significant variations among known face images. We use this weight vector to recognize each individual. Several evaluation methods of this weight vector are attempted in this paper.

Fast feature extraction method for robust face verification

Abstract: A feature extraction technique for face verification is proposed. It utilises polynomial coefficients derived from 2D discrete cosine transform (DCT) coefficients of neighbouring blocks. Experimental results suggest that the technique is more robust against illumination direction changes than 2D Gabor wavelets, 2D DCT and eigenface methods. Moreover, compared to Gabor wavelets, the proposed technique is over 80 times quicker to compute.

Optimal FLD algorithm for facial feature extraction

Abstract: In this paper, we try to extend Fisher linear discriminant analysis (FLD) to the singular cases. Firstly, PCA is used to reduce the dimension of feature space to N-1 (N denotes the number of training samples). Then, the transformed space is divided into two subspaces: the null space of within- class scatter matrix and its orthogonal complement, from which two cases of optimal discriminant vectors are selected respectively. Finally, we test our method on ORL face database, and achieve a recognition rate of 97% with a minimum distance classifier or a nearest neighbor classifier. The experimental results indicate that our approach is better than classical Eigenfaces and Fisherfaces with respect to recognition performance.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.