Face recognition with image sets using manifold density divergence

By Ognjen Arandjelovic, Gregory Shakhnarovich, John W. Fisher III, Roberto Cipolla, Trevor Darrell. Computer Vision and Pattern Recognition, 2005

339 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fae74cbc5a090ff5a323a48d31901c58db7a1a65

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (2015, background): “Another type of SPD matrix is the resulting matrix of Gaussian distribution, which is exploited by a few works (Shakhnarovich et al., 2002; Arandjelović et al., 2005; Huang et al., 2015b; Wang et al., 2015) to capture the entire probabilistic model of object variations for more robust image set clas”
Deep Reconstruction Models for Image Set Classification (2015, background): “Recognition from these multiple images is commonly formulated as an image set classification problem and has gained significant attention from the research community in recent years [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14].”
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011, background): “While image set matching can be accomplished through probability-density based methods [3, 8] and aggregation methods [17], it has been shown that better performance can be attained through modelling image sets via linear structures (ie.”
Manifold Discriminant Analysis (2009, methodology): “Typical model-based methods [1], [21], tend to represent image set by parametric distribution function, and then measure the similarity between two distributions.”
Universal Estimation of Information Measures for Analog Sources (2009, neutral): “In the area of image processing, divergence estimates have been applied to texture classification [77, 163, 266], shape and radiance estimation [84], and face recognition [12, 215].”
A 3D Face Model for Pose and Illumination Invariant Face Recognition (2009, background): “Numerous face recognition articles acknowledge the fact that 3DMM based face image analysis constitutes the state of the art, but note that the main obstacle resides in the complications of their construction (e.g. [22] [13] [12] [5]).”
Covariance discriminative learning: A natural and efficient approach to image set classification (2012, methodology): “Parametric modeling methods seek to represent each image set with some parametric distribution function, e.g., single Gaussian [22] or Gaussian mixture models (GMM) [1], and then measure the similarity between two distributions in terms of the Kullback-Leibler Divergence (KLD).”
Manifold-Manifold Distance with application to face recognition based on image set (2008, methodology): “Representative parametric methods include [ 1 ], [19].”
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations (2007, methodology): “Relevant previous approaches to set matching for set classification can be broadly partitioned into parametric model-based [17], [34] and nonparametric sample-based methods [12], [14].”
Discriminability Distillation in Group Representation Learning (2020, methodology): “To tackle set-to-set face recognition, traditional methods directly estimate the feature similarity among sets of feature vectors [1,23,5].”
Probabilistic Face Embeddings (2019, background): “In the field of face template/video matching, there exists abundant literature on modeling the faces as probabilistic distributions [29, 1], subspace [3] or manifolds [1, 11] in the feature space.”