Robust Recognition Using Eigenimages

By Ales Leonardis, Horst Bischof. Computer Vision and Image Understanding, 2000

327 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0039685527d20ee4a100837be9185892f1a4d8bf

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In Computer Vision and Pattern Recognition

Citations in the corpus

Locally Optimized RANSAC (2003, methodology): “RANSAC has been applied in the context of short baseline stereo [13, 12], wide baseline stereo matching [9, 15, 10, 6], motion segmentation [13], mosaicing [7], detection of geometric primitives [2], robust eigenimage matching [5] and elsewhere.”
Spectral embedding of graphs (2003, background): “This approach was 0rst introduced by Murase and Nayar [17], and has since been re0ned in a number of di4erent ways [28,29].”
Linear Regression for Face Recognition (2010, background): “Reconstructive approaches (such as PCA and ICA) are reported to be robust for the problem of contaminated pixels [6], whereas discriminative approaches (such as LDA) are known to yield better results in clean conditions [7].”
Maximum Correntropy Criterion for Robust Face Recognition (2011, methodology): “…facial features, a number of algorithms, such as modular Eigenspaces [4], modular linear regression based classification (LRC) [5], eigenimages [6], [7], [8], minimax probability machine [9], reconstructive and discriminative subspace method [10], and associative memories [11], [12], were…”
On-line Boosting and Vision (2006, background): “As such, it enables “hallucinations” and comparison of the visual input with the stored model [67].”
Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition (2007, methodology): “To eliminate the extrinsic factors, various feature extraction and selection methods are widely used [1]–[4].”
Learning from facial aging patterns for automatic age estimation (2006, background): “Note that the aging patterns are highly redundant, it is possible to estimate yk only based on part of xk [14], i.”
Automatic Age Estimation Based on Facial Aging Patterns (2007, background): “Note that the aging patterns are highly redundant; it is possible to estimate yk only based on part of xk [14], say x a k.”
Appearance-based face recognition and light-fields (2004, methodology): “(This algorithm is closely related to an algorithm for dealing with occlusions in the eigen-space approach [7], [16].)”
Sparse Representation for Signal Classification (2006, methodology): “In [17, 19], PCA method with subsampling effectively detects and excludes outliers for the following LDA analysis.”
Randomized RANSAC with Td, d test (2004, methodology): “RANSAC has been applied in the context of short baseline stereo [11, 13], wide baseline stereo matching [8, 14, 10], motion segmentation [11], mosaicing [6], detection of geometric primitives [1], robust eigenimage matching [4] and elsewhere.”
50 Years of object recognition: Directions forward (2013, methodology): “Leonardis and Bischof [148] ** ** ** ** ** * * *”
Robust Face Recognition via Sparse Representation (2009, background): “…the given n i training samples from the i -th class as columns of a matrix In the context of face recognition, we will identify a w × h grayscale image with the vector v ∈ R m ( m = wh ) given by stacking its columns; the columns of A i are then the training face images of the i -th subject.”