Low-dimensional procedure for the characterization of human faces.

By Lawrence Sirovich, Michael Kirby. 1987

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Generative Modeling by Estimating Gradients of the Data Distribution (2019, background): “l manifolds, the images are likely to be conﬁned to manifolds with even smaller dimensions. Many similar observations related to the manifold hypothesis have been reported in prior work. For example, [42] reported that face images can be constructed from linear combinations of a small number of typi”
Example-Based Learning for View-Based Human Face Detection (1998, positive): “Kirby and Sirovich [16] [9] have used principal components analysis methods to build low-dimensional mod-”
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions (2009, methodology): “To identify the subject in a new picture, we compute its decomposition in this basis and use a classification technique, such as nearest neighbors, to select the closest image in the database [122].”
An improved face recognition technique based on modular PCA approach (2004, methodology): “Approximate reconstruction of faces in the ensemble was performed using a weighted combination of eigenvectors (eigenpictures), obtained from that ensemble (Sirovich and Kirby, 1987).”
A new ranking method for principal components analysis and its application to face image analysis (2010, background): “Since the pioneering work of Sirovich and Kirby [12], published more than 20 years ago, several subsequent works have projected face images on a PCA feature space to not only reduce the dimensionality of the original samples for further classification and analysis but also to interpret and reconstru”
Eigenfaces vs . Fisherfaces : Recognition Using Class Speci c Linear Projection (2001, methodology): “Sirovitch and Kirby found a similar point of diminishing returns when using Eigenfaces to represent face images [17].”
Multilinear independent components analysis (2005, methodology): “In particular, the linear, appearance-based face recognition method known as Eigenfaces [9] is founded on the principal components analysis (PCA) of facial image ensembles [7].”
Appearance-based face recognition and light-fields (2004, background): “Although the expression " appearance-based " was introduced by Murase and Nayar [19], the approach itself dates back to Turk and Pentland's Eigenfaces [28] and perhaps before [27].”
From few to many: generative models for recognition under variable pose and illumination (2000, methodology): “A technique now commonly used in computer vision|particularly in face recognition|is principal components analysis PCA which is popularly known as Eigenfaces 8, 12, 1 3 , 1 9 .”
Preserving privacy by de-identifying face images (2005, methodology): “A covariance (or scatter) matrix is the product of the transpose of A with itself AA, but due to the structure of face recognition data, namely, M << N , the variable C is computed using AA, yielding a square matrix of dimension M [20].”
Labeled Faces in the Wild: A Survey (2016, methodology): “Turk et al. [99] introduce the eigenpicture method by Sirovich [86] to face recognition .”