Proceedings ArticleDOI
Manifold Discriminant Analysis
Ruiping Wang,Xilin Chen +1 more
- pp 429-436
TLDR
The proposed MDA method is evaluated on the tasks of object recognition with image sets, including face recognition and object categorization, and seeks to learn an embedding space, where manifolds with different class labels are better separated, and local data compactness within each manifold is enhanced.Abstract:
This paper presents a novel discriminative learning method, called manifold discriminant analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we formulate the problem as classification-oriented multi-manifolds learning. Aiming at maximizing “manifold margin”, MDA seeks to learn an embedding space, where manifolds with different class labels are better separated, and local data compactness within each manifold is enhanced. As a result, new testing manifold can be more reliably classified in the learned embedding space. The proposed method is evaluated on the tasks of object recognition with image sets, including face recognition and object categorization. Comprehensive comparisons and extensive experiments demonstrate the effectiveness of our method.read more
Citations
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Proceedings ArticleDOI
Covariance discriminative learning: A natural and efficient approach to image set classification
TL;DR: A novel discriminative learning approach to image set classification by modeling the image set with its natural second-order statistic, i.e. covariance matrix, which shows the superiority of this method over state-of-the-art ones in both accuracy and efficiency, but also its stability to two real challenges: noisy set data and varying set size.
Proceedings ArticleDOI
Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition
TL;DR: This paper attempts to solve temporal alignment and semantics-aware dynamic representation problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet, and reports results better than the known state-of-the-art.
Journal ArticleDOI
Discriminative Multimanifold Analysis for Face Recognition from a Single Training Sample per Person
Jiwen Lu,Yap-Peng Tan,Gang Wang +2 more
TL;DR: This paper proposes a novel discriminative multimanifold analysis (DMMA) method by learning discrim inative features from image patches by partitioning each enrolled face image into several nonoverlapping patches to form an image set for each sample per person.
Proceedings ArticleDOI
Sparse approximated nearest points for image set classification
Yiqun Hu,Ajmal Mian,Robyn Owens +2 more
TL;DR: This paper introduces a novel between-set distance called Sparse Approximated Nearest Point (SANP) distance, which enforces sparsity on the sample coefficients rather than the model coefficients and jointly optimizes the nearest points as well as their sparse approximations.
Proceedings ArticleDOI
Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition
TL;DR: This work proposes a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space, which can be regarded as performing a geometry-aware dimensionality reduction from the original Grassmann manifolds to a lower-dimensional, more discriminative Grassman manifold where more favorable classification can be achieved.
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