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Dual Linear Regression Based Classification for Face Cluster Recognition

https://doi.org/10.1109/cvpr.2014.342

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Linear Regression Analysis

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Face Recognition from Long-Term Observations

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Set Based Discriminative Ranking for Recognition

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Computing LTS Regression for Large Data Sets

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Incremental Learning for Robust Visual Tracking

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From still image to video-based face recognition: an experimental analysis

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Video-based face recognition using probabilistic appearance manifolds

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Face Recognition with Image Sets Using Manifold Density Divergence

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Learning a Spatially Smooth Subspace for Face Recognition

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Face tracking and recognition with visual constraints in real-world videos

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Manifold-Manifold Distance with application to face recognition based on image set

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Manifold Discriminant Analysis

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Face recognition based on image sets

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Sparse approximated nearest points for image set classification

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Covariance discriminative learning: A natural and efficient approach to image set classification

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Manifold Discriminant Analysis

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Face recognition based on regularized nearest points between image sets

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Orthogonal Laplacianfaces for Face Recognition

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SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis

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The CMU pose, illumination, and expression database

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Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

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Robust Face Recognition via Sparse Representation

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Linear Regression for Face Recognition

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Face Recognition Using Sparse Approximated Nearest Points between Image Sets

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ROBUSTNESS INSTEAD OF ACCURACY SHOULD BE THE PRIMARY OBJECTIVE FOR SUBJECTIVE PATTERN RECOGNITION RESEARCH: STABILITY ANALYSIS ON MULTICANDIDATE ELECTORAL COLLEGE VERSUS DIRECT POPULAR VOTE

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Eigenfaces for Recognition

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Linear Regression Analysis

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Image Set-Oriented Dual Linear Discriminant Regression Classification and Its Kernel Extension

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Survey: How good are the current advances in image set based face identification? – Experiments on three popular benchmarks with a naïve approach

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