Background learning for robust face recognition with PCA in the presence of clutter
TL;DR: A new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter by learning the distribution of background patterns and it is shown how this can be done for a given test image.
Abstract: We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms.
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20 Apr 2009
TL;DR: This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications.
Abstract: The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas
721 citations
TL;DR: A basic introduction to multivariate analysis techniques is provided with sample applications to simulated and real-world data sets, highlighting the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community.
Abstract: As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.
114 citations
TL;DR: A novel face segmentation algorithm based on facial saliency map (FSM) for head-and-shoulder type video application that is capable of segmenting the face area quite effectively.
88 citations
Additional excerpts
...E-mail addresses: hlli@ee.cuhk.edu.hk (H. Li), k Ngan)....
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...All rights reserved....
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TL;DR: In this paper, the distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis, and three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces.
Abstract: Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis. Three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.
40 citations
References
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12 Mar 1997
611 citations
01 Jan 1998
TL;DR: A hybrid classifier using PCA and LDA provides a useful framework for other image recognition tasks as well and demonstrates a significant improvement when principal components rather than original images are fed to the LDA classifier.
Abstract: In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: first we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a linear classifier. The basic idea of combining PCA and LDA is to improve the generalization capability of LDA when only few samples per class are available. Using FERET dataset we demonstrate a significant improvement when principal components rather than original images are fed to the LDA classifier. The hybrid classifier using PCA and LDA provides a useful framework for other image recognition tasks as well.
539 citations
TL;DR: The paper examines the mathematical tools that have proven successful, provides a taxonomy of the problem domain, and then examines the state of the art: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces.
Abstract: The research topic of looking at people, that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is "fourth generation" embedded computing: "smart" environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. The paper examines the mathematical tools that have proven successful, provides a taxonomy of the problem domain, and then examines the state of the art. Four areas receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces. Finally, the paper discusses some of the research challenges and opportunities.
436 citations
TL;DR: EP has better recognition performance than PCA (eigenfaces) and better generalization abilities than the Fisher linear discriminant (Fisherfaces).
Abstract: Introduces evolutionary pursuit (EP) as an adaptive representation method for image encoding and classification In analogy to projection pursuit, EP seeks to learn an optimal basis for the dual purpose of data compression and pattern classification It should increase the generalization ability of the learning machine as a result of seeking the trade-off between minimizing the empirical risk encountered during training and narrowing the confidence interval for reducing the guaranteed risk during testing It therefore implements strategies characteristic of GA for searching the space of possible solutions to determine the optimal basis It projects the original data into a lower dimensional whitened principal component analysis (PCA) space Directed random rotations of the basis vectors in this space are searched by GA where evolution is driven by a fitness function defined by performance accuracy (empirical risk) and class separation (confidence interval) Accuracy indicates the extent to which learning has been successful, while separation gives an indication of expected fitness The method has been tested on face recognition using a greedy search algorithm To assess both accuracy and generalization capability, the data includes for each subject images acquired at different times or under different illumination conditions EP has better recognition performance than PCA (eigenfaces) and better generalization abilities than the Fisher linear discriminant (Fisherfaces)
343 citations
TL;DR: It is described how two-dimensional face images can be converted into one-dimensional sequences to allow similar techniques to be applied and how a HMM can be used to automatically segment face images and extract features that can be use for identification.
343 citations