Eigenface

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

Principles and Methods for Face Recognition and Face Modelling

Abstract: This chapter focuses on the principles behind methods currently used for face recognition, which have a wide variety of uses from biometrics, surveillance and forensics. After a brief description of how faces can be detected in images, we describe 2D feature extraction methods that operate on all the image pixels in the face detected region: Eigenfaces and Fisherfaces first proposed in the early 1990s. Although Eigenfaces can be made to work reasonably well for faces captured in controlled conditions, such as frontal faces under the same illumination, recognition rates are poor. We discuss how greater accuracy can be achieved by extracting features from the boundaries of the faces by using Active Shape Models and, the skin textures, using Active Appearance Models, originally proposed by Cootes and Talyor. The remainder of the chapter on face recognition is dedicated such shape models, their implementation and use and their extension to 3D. We show that if multiple cameras are used the the 3D geometry of the captured faces can be recovered without the use of range scanning or structured light. 3D face models make recognition systems better at dealiing with pose and lighting variation

A Statistical Discriminant Model for Face Interpretation and Reconstruction

Abstract: Multivariate statistical approaches have played an important role of recognising face images and characterizing their differences. In this paper, we introduce the idea of using a two-stage separating hyper-plane, here called Statistical Discriminant Model (SDM), to interpret and reconstruct face images. Analogously to the well-known Active Appearance Model proposed by Cootes et. al, SDM requires a previous alignment of all the images to a common template to minimise variations that are not necessarily related to differences between the faces. However, instead of using landmarks or annotations on the images, SDM is based on the idea of using PCA to reduce the dimensionality of the original images and a maximum uncertainty linear classifier (MLDA) to characterise the most discriminant changes between the groups of images. The experimental results based on frontal face images indicate that the SDM approach provides an intuitive interpretation of the differences between groups, reconstructing characteristics that are very subjective in human beings, such as beauty and happiness.

A novel approach based on variance for local feature analysis of facial images

Abstract: A low dimensional representation of sensory signals is a key for solving many of the computational problems encountered in high level vision. In this paper, a comparison of face recognition techniques using principal component analysis (PCA) is done with local feature analysis (LFA) and an alternate method based on variance for quickly finding the local feature points on face images is also proposed. The LFA method is an extension of the eigenfaces method and gives a low-dimensional output for face representation. Principal component analysis (PCA) that is used for dimensionality reduction in the eigenfaces technique leads to global outputs, which are non-topographic and are not biologically plausible. On the other hand, the local feature analysis (LFA) technique yields local, topographic outputs which are sparsely distributed. They are effectively low dimensional but retain all the characteristics of the global modes. Local representations are desirable since they offer robustness against variability due to changes in the localised regions of the objects. A strategy for recognising faces using LFA is also proposed and several results on reconstruction and recognition are given to compare the performance of the variance method with that of LFA and PCA.

Connected Component Level Discrimination of Handwritten and Machine-Printed Text Using Eigenfaces

Abstract: We employ Eigenfaces to discriminate between handwritten and machine-printed text at the connected component (CC) level. Normalized images of machine print CCs are treated as points in a high-dimensional space. PCA yields a reduced-dimensional character space. Representative machine print CCs are projected into character space and a local distance threshold for each representative is automatically determined. CCs are classified as machine print if they are within the local distance threshold of their closest machine print representative. Otherwise, they are classified as handwriting. Recursive character segmentation using min graph cut is used to address the problem of touching characters. Validation over a large NIST handwriting and machine print database demonstrates precision of 93.98% and 89.1% for machine print and handwriting respectively.

Face Identification from Manipulated Facial Images using SIFT

Abstract: Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods.