The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Face recognition based on image sets
Open accessAbstract
We introduce a novel method for face recognition from image sets. In our setting each test and training example is a set of images of an individual's face, not just a single image, so recognition decisions need to be based on comparisons of image sets. Methods for this have two main aspects: the models used to represent the individual image sets; and the similarity metric used to compare the models. Here, we represent images as points in a linear or affine feature space and characterize each image set by a convex geometric region (the affine or convex hull) spanned by its feature points. Set dissimilarity is measured by geometric distances (distances of closest approach) between convex models. To reduce the influence of outliers we use robust methods to discard input points that are far from the fitted model. The kernel trick allows the approach to be extended to implicit feature mappings, thus handling complex and nonlinear manifolds of face images. Experiments on two public face datasets show that our proposed methods outperform a number of existing state-of-the-art ones.
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Cites
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A Global Geometric Framework for Nonlinear Dimensionality Reduction
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Face Description with Local Binary Patterns: Application to Face Recognition
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R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
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Training Invariant Support Vector Machines
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Geometric Representation of High Dimension, Low Sample Size Data
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Cited by
Neural Aggregation Network for Video Face Recognition
Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.554 · Cited by 409
Quality Aware Network for Set to Set Recognition
Liu, Y., Yan, J., & Ouyang, W. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.499 · Cited by 339
Partial least squares regression on grassmannian manifold for emotion recognition
Liu, M., Wang, R., Huang, Z., Shan, S., & Chen, X. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531738 · Cited by 62
Metadata
- Authors
- Cevikalp, H., & Triggs, B.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 2567-2573
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1996939238
- Cited by
- 489
- References
- 24
- FWCI
- 21.72
- Type
- conference paper
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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