Eigenface

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

Face image analysis by unsupervised learning and redundancy reduction

Abstract: In a task such as face recognition, much of the important information may be contained in the high-order relationships among the image pixels. Representations such as "Eigenfaces" (197) and "Holons" (48) are based on Principal component analysis (PCA), which encodes the correlational structure of the input, but does not address high-order statistical dependencies such as relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA which encodes the high-order dependencies in the input in addition to the correlations. Representations for face recognition were developed from the independent components of face images. The ICA representations were superior to PCA for recognizing faces across sessions and changes in expression. ICA was compared to more than eight other image analysis methods on a task of recognizing facial expressions in a project to automate the Facial Action Coding System (62). These methods included estimation of optical flow; representations based on the second-order statistics of the full face images such Eigenfaces (47, 197) local feature analysis (156), and linear discriminant analysis (23); and representations based on the outputs of local filters, such as a Gabor wavelet representations (50, 113) and local PCA (153). The ICA and Gabor wavelet representations achieved the best performance of 96% for classifying 12 facial actions. Relationships between the independent component representation and the Gabor representation are discussed. Temporal redundancy contains information for learning invariances. Different views of a face tend to appear in close temporal proximity as the person changes expression, pose, or moves through the environment. The final chapter modeled the development of viewpoint invariant responses to faces from visual experience in a biological system by encoding spatio-temporal dependencies. The simulations combined temporal smoothing of activity signals with Hebbian learning (72) in a network with both feed-forward connections and a recurrent layer that was a generalization of a Hopfield attractor network. Following training on sequences of graylevel images of faces as they changed pose, multiple views of a given face fell into the same basin of attraction, and the system acquired representations of faces that were approximately viewpoint invariant.

Face Recognition Using an Enhanced Independent Component Analysis Approach

Abstract: This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself

Face recognition using Principal Component Analysis

Abstract: The strategy of face recognition involves the examination of facial features in a picture, recognizing those features and matching them to 1 of the many faces in the database. There are lots of algorithms effective at performing face recognition, such as for instance: Principal Component Analysis, Discrete Cosine Transform, 3D acceptance methods, Gabor Wavelets method etc. This work has centered on Principal Component Analysis (PCA) method for face recognition in an efficient manner. There are numerous issues to take into account whenever choosing a face recognition method. The main element is: Accuracy, Time limitations, Process speed and Availiability. With one of these in minds PCA way of face recognition is selected because it is really a simplest and easiest approach to implement, extremely fast computation time. PCA (Principal Component Analysis) is an activity that extracts the absolute most relevant information within a face and then tries to construct a computational model that best describes it.

Face recognition with eigenfaces

Abstract: Develops an approach to face recognition using eigenfaces, focusing on the effects of the eigenface used to represent a human face under several environment conditions. The authors first derive some computational feasible formula to find the eigenfaces, then investigate the relationship of mean absolute error between original face images and reconstructed images under various conditions such as face size, lighting and head orientation changes. The experimental results show that a large number of eigenfaces are not necessary to describe an individual face and only about 80 eigenfaces are sufficient for a large size set of face images. Gaussian smoothing can minimize the error under the same conditions. Finally, a face recognition system with eigenfaces and backpropagation neural network is implemented. >

Embedded Bayesian networks for face recognition

Abstract: The embedded Bayesian networks (EBN) introduced in this paper, are a generalization of the embedded hidden Markov models previously used for face and character recognition An EBN is defined recursively as a hierarchical structure where the "parent" node is a Bayesian network (BN) that conditions the EBNs or the observation sequence that describes the nodes of the "child" layer With an EBN, one can model complex N-dimensional data, avoiding the complexity of N-dimensional BNs while still preserving their flexibility and partial scale invariance In this paper we present an application of the EBNs for face recognition and show the improvement of this approach versus the "eigenface" and the embedded HMM approaches