Characterising Virtual Eigensignatures for General Purpose Face Recognition

Graham, Daniel B.; Allinson, Nigel M.

doi:10.1007/978-3-642-72201-1_25

Cited by 481 publications

(235 citation statements)

References 12 publications

Supporting

Mentioning

231

Contrasting

Order By: Relevance

“…In this experiment, we used a database (Graham and Allinson 1997) of 250 images of 18 subjects, each acquired at 25 different head poses (see some views in Figs. 2 and 4).…”

Section: How Components Capture Pixels' Correlationsmentioning

confidence: 99%

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

et al. 2012

View full text Add to dashboard Cite

pp. 756-763, 2005); they are often referred as "stel models." Their main characteristic is to segment objects in clear, often semantic, parts as a consequence of the modeling constraint which forces the regions belonging to a single segment to have a tight distribution over local measurements, such as color or texture. This self-similarity within a region in a single image is typical of many meaningful image parts, even when across different images of similar objects, the corresponding parts may not have similar local measurements. Moreover, the segmentation itself is expected to be consistent within a class, although still flexible. These models have been applied mostly to segmentation scenarios.In this paper, we extent those ideas (1) proposing to capture correlations that exist in structural elements of an image class due to global effects, (2) exploiting the segmentations to capture feature co-occurrences and (3) allowing the use of multiple, eventually sparse, observation of different nature. In this way we obtain richer models more suitable to recognition tasks. We accomplish these requirements using a novel approach we dubbed stel component analysis. Experimental results show the flexibility of the model as it can deal successfully with image/video segmentation and object recognition where, in particular, it can be used as an alternative of, or in conjunction with, bag-of-features and related classifiers, where stel inference provides a meaningful spatial partition of features.

show abstract

“…In this experiment, we used a database (Graham and Allinson 1997) of 250 images of 18 subjects, each acquired at 25 different head poses (see some views in Figs. 2 and 4).…”

Section: How Components Capture Pixels' Correlationsmentioning

confidence: 99%

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

et al. 2012

View full text Add to dashboard Cite

show abstract

“…In this work, the algorithms are evaluated with two widely used face databases, UMIST [8] and FERET [19]. The UMIST repository is a multi-view database, consisting of 575 images of 20 people, each covering a wide range of poses from profile to frontal views [8]. The FERET database has been considered current most comprehensive and representative face database [19,20].…”

Section: Face Recognition Face Recognition Evaluation Designmentioning

confidence: 99%

“…We would like to thank the FERET Technical Agent, the U.S. National Institute of Standards and Technology (NIST) for providing the FERET database. Also, We would like to thank Dr. Daniel Graham and Dr. Nigel Allinson for providing the UMIST face database [8].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Kernel Discriminant Learning with Application to Face Recognition

Venetsanopoulos

Rogers

2005

Support Vector Machines: Theory and Applications

View full text Add to dashboard Cite

Abstract. When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often suffer from two problems: 1) small training sample size compared to the dimensionality of the sample (or mapped kernel feature) space, and 2) high computational complexity. In this chapter, we introduce a new kernel discriminant learning method, which attempts to deal with the two problems by using regularization and subspace decomposition techniques. The proposed method is tested by extensive experiments performed on real face databases. The obtained results indicate that the method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel Principal Component Analysis and kernel Linear Discriminant Analysis, at a significantly reduced computational cost.

show abstract

“…In this experiment, face image data of 5 subjects derived from University of Manchester Institute of Science and Technology (UMIST) face database (15) were used. The face images are taken sequentially from frontal pose to profile under constant illuminant condition and immovable face.…”

Section: Face Recognition Under Varying Posementioning

confidence: 99%

Nonlinear Adaptive Manifold Self-Organizing Map with Reproducing Kernels and its Application to Pose Invariant Face Recognition

2005

View full text Add to dashboard Cite

Adaptive Manifold Self-Organizing Map (AMSOM) is an evolution of Self-Organizing Maps, where each computational unit defines a linear affine subspace (manifold). The affine subspace in a unit is represented by a mean vector and a set of basis vectors. After training, these units result in a set of affine subspace detectors. In complex situations, however, these are not enough to describe a class of patterns because of its linearity. In this paper, the AMSOM in the high-dimensional space with reproducing kernels, referred to as Nonlinear Adaptive Manifold Self-Organizing Map (NAMSOM) is proposed in order to achieve efficient classification and visualization of relations between classes. By using the reproducing kernels, linear affine subspaces in the AMSOM can be extended to nonlinear affine subspaces easily. To verify the effectiveness of the proposed method, it was applied to face recognition under varying pose as a practical example.

show abstract

Characterising Virtual Eigensignatures for General Purpose Face Recognition

Cited by 481 publications

References 12 publications

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

Stel Component Analysis: Joint Segmentation, Modeling and Recognition of Objects Classes

Kernel Discriminant Learning with Application to Face Recognition

Nonlinear Adaptive Manifold Self-Organizing Map with Reproducing Kernels and its Application to Pose Invariant Face Recognition

Contact Info

Product

Resources

About