Eigenface

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

Face Class Modeling Using Mixture of SVMs

Abstract: We present a method for face detection which uses a new SVM structure trained in an expert manner in the eigenface space. This robust method has been introduced as a post processing step in a real-time face detection system. The principle is to train several parallel SVMs on subsets of some initial training set and then train a second layer SVM on the margins of the first layer of SVMs. This approach presents a number of advantages over the classical SVM: firstly the training time is considerably reduced and secondly the classification performance is improved, we will present some comparisions with the single SVM approach for the case of human face class modeling.

Improved nuisance attribute projection for face recognition

Abstract: Th e illumination variation is one of the well-known problems in face recognition under uncontrolled environments. Several techniques have been presented in the literature to cope up with this problem. Lately, a technique known as Nuisance Attribute Projection (NAP), originally developed for the speaker recognition field was introduced to image processing in order to compensate for luminance artifacts. This paper extends and improves the earlier work by exploring efficient methodologies for using NAP for face recognition under varied illumination conditions. In particular, we propose a modified NAP formulation and show that NAP training can be simplified for face recognition. Additionally, we suggested a compact framework merging between NAP compensation and eigenface recognition. A series of experiments using the extended YaleB database, and a cross-validation using the PIE CMU and the Oulo databases are performed to validate our proposals.

Face recognition method using mutual information and hybrid feature

Abstract: A two-stage face recognition algorithm is proposed. In the first stage, the mutual information match is applied to reduce the candidate pattern amount in the database. In the second stage, the principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the probe image to extract the corresponding features, which will be used in classification. The approach is tested on ORL database, Shimon database and Harvard database. The experimental results demonstrate that the performance of this system is superior to other algorithms on the same database.

Performance analysis of face recognition algorithms

Abstract: The usage areas of biometric systems are becoming widespread in today's technology. Face recognition systems among biometric systems; Ease of use, reliability, cost, etc., the preference between public institutions, commercial enterprises and researchers is increasing. In this study, it is suggested that students should use face recognition system instead of traditional methods of absenteeism in education and training institutions. It is very important that face recognition systems work quickly with matching people correctly. In this study, the training and recognition times of Eigenfaces, Fisherfaces and Local Binary Pattern algorithms used in face recognition systems are calculated by using Visual C ++ and Python programming languages using ORL dataset.

Decomposition and virtualization of eigenface for face recognition under various lighting conditions

Abstract: It is intended in this paper to construct a recognition method that is more robust against changes of lighting conditions than the conventional method. The method applies orthogonal decomposition and virtualization to the eigenface within the framework of the subspace method. The eigenface is derived by applying principal component analysis (eigenvalue decomposition) to the set of face images for each person under various lighting conditions. In the proposed method, the standard face eigenspace (canonical space) is used, and the face image is orthogonally decomposed into the projection component, which is affected mostly by the lighting variation, and the residual component, which contains other aspects of personality and noise. By analyzing the residual component, the noise component in the input image is eliminated. By the above approach, the eigenspace (eigen-projection) containing the standard face information, and the eigenspace (eigen-residual) containing the individuality, are constructed. To the eigen-projection, the concept of the virtual subspace is applied, and the virtual eigen-projection is constructed. With this procedure, stable recognition is realized using the subspace method, even if only a small number of images (as few as one) are registered. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 36(1): 25–34, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.10638