Eigenface

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

Video Face Recognition: A Physiological and Behavioural Multimodal Approach

Abstract: In this article we present a multimodal system to person recognition by integrating two complementary approaches that work with video data. The first module exploits the behavioural information: it is based on statistical features computed using the displacement signals of a head; the second one is dealing with the physiological information: it is a probabilistic extension of the classic Eigenface approach. For a consistent fusion, both systems share the same probabilistic classification framework: a Gaussian mixture model (GMM) approximation and a Bayesian classifier. We assess the performances of the multimodal system by implementing two fusion strategies and we analyse their evolution in presence of artificial noise.

Face recognition using KFD-Isomap

Abstract: Facial images with high dimension often belong to a manifold of intrinsically low dimension. Subspace methods utilize different algorithms to extract and analyze the underlying manifold for face recognition. Isomap is a recently proposed algorithm for manifold learning and nonlinear dimensionality reduction. However, since the Isomap is developed based on minimizing the reconstruction error with multi-dimensional scaling, it may not be optimal from classification viewpoint. In this paper, an improved version of Isomap, namely KFD-Isomap, is proposed using kernel Fisher discriminant (KFD) method for face recognition task. In KFD-Isomap, the matrix of geodesic distances between all pairs of points as feature vectors is applied to the kernel Fisher discriminant for finding an optimal projection direction. In face recognition experiments, KFD-Isomap is used as a feature extraction process compared with Isomap, Ext-Isomap, and two other baseline subspace algorithms, eigenfaces and Fisherfaces, combined with a nearest neighbor classifier. Experimental results show that KFD-Isomap excels the other methods.

Feature selection for face recognition:a memetic algorithmic approach

Abstract: The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.

A MATLAB based Face Recognition usingPCA with Back Propagation Neural network

Abstract: Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. There are many techniques used for this purpose. Face recognition is an effective means of authenticating a person. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for face recognition. The system consists of a database of a set of facial patterns for each individual. The characteristic features of pca called „eigenfaces‟ are extracted from the stored images, which is combine with Back Propagation Neural Network for subsequent recognition of new images.

Face detection using an SVM trained in eigenfaces space

Abstract: The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling.