Eigenfaces vs . Fisherfaces : Recognition Using Class Speci c Linear Projection

By Peter N. Belhumeur, David J. Kriegman. 2001

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Citations in the corpus

Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016, background): “FengandPrabhakaran proposed a system to help artists come up with makeup and hair designs that can be used to prevent FRSs (including Eigenfaces [40] and Fisherfaces [1]) from recognizing subjects [10].”
Deep Learning Face Representation by Joint Identification-Verification (2014, neutral): “It can be traced back to early subspace face recognition methods such as LDA [1], Bayesian face [17], and uniﬁed subspace [23, 24].”
Frankenstein: Learning Deep Face Representations Using Small Data (2016, unknown): “Conventional features include linear functions of the raw pixel values, including Eigenface (Principal Component Analysis) [56], Fisherface (Linear Discriminant Analysis) [3], and Laplacianface (Locality Preserving Pro-jection) [14].”
Discriminative K-SVD for dictionary learning in face recognition (2010, background): “Well-known, conventional approaches include Eigenface [21] and Fisherface [3], among others.”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, methodology): “Note that there are many other dimensionality reduction methods that could be applied – notably local or manifold-based representations such as [51] – but in this paper we will focus on global linear vector and tensor reductions.”
Jet-images: computer vision inspired techniques for jet tagging (2014, background): “In facial recognition, Principle Components Analysis (PCA) [49] and Fisher’s Linear Discriminant (FLD) [50] are common choices and their speciﬁed directions are known as eigenfaces and ﬁsherfaces [51] respectively.”
Robust Face Recognition via Sparse Representation (2009, background): “16 Notice that Fisherfaces are different from the other features because the maximal number of valid Fisherfaces is one less than the number of classes k [24], 38 in this case.”
Assisting Users in a World Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications (2017, background): “Jafri et al. provide a comprehensive survey of the evolution of facial recognition technology [37], including breakthrough systems such as Eigen-faces [51] and Fisherfaces [27].”
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011, methodology): “While LDA has been successfully applied to various computer vision problems, eg., face recognition [5], it suffers from not being able to naturally capture the local structure of data [10, 24].”
Face classification using electronic synapses (2017, methodology): “2c, the other 24 cropped and down-sampled face images from the Yale Face Database are used to evaluate the perceptron’s generation ability, as shown in Supplementary Fig.”
Probabilistic Linear Discriminant Analysis for Inferences About Identity (2007, methodology): “The Fisherfaces al-gorithm [1] projected face data to a space where the ratio of between-individual variation to within-individual variation was maximized.”