Evolutionary Pursuit and Its Application to Face Recognition

By Chengjun Liu, Harry Wechsler. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

379 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cada4a2cee3530bb762c5df439a5f671aeea47e3

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Citations in the corpus

Recent advances in visual and infrared face recognition - a review (2005, background): “[140] compared the relative performances of the three face recognition algorithms for visible and IR images: matching pursuit filters [141,142], gray-scale projection, and eigenface techniques [109,143,144].”
Gabor-based kernel PCA with fractional power polynomial models for face recognition (2004, methodology): “Popular representation methods for face recognition include Principal Component Analysis (PCA) [22], [47], [26], shape and texture (‘shape-free’ image) of faces [9], [4], [49], [24], [27], and Gabor wavelet representation [12], [31], [23], [50], [28].”
A Survey of Face Recognition Techniques (2009, methodology): “Some other AI approaches utilized for the face recognition task include evolutionary pursuit [181, 182] and techniques [183, 184] based on boosting [161, 185].”
Human face recognition based on multidimensional PCA and extreme learning machine (2011, methodology): “An eigenspace based adaptive approach that utilizes a specific kind of genetic algorithm called evolutionary pursuit (EP) [8] and elastic bunch graph matching (EBGM) [9] have also been proposed to generate an optimal set of projection axes.”
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, methodology): “The proposed method can be considered as an extension to evolutionary algorithms (Liu and Wechsler 2000) in which only one optimal basis is sought for all individuals.”
Independent component analysis of Gabor features for face recognition (2003, methodology): “Another possibility is to search, using the evolutionary pursuit (EP) method [ 25 ], for the sparse features directly with the twin goals of reducing the amount of data used for classification and simultaneously providing enhanced discriminatory power.”
Face recognition: A literature survey (2003, methodology): “…et al. 1997; Swets and Weng 1996b; Zhao et al. 1998] SVM Two-class problem based on SVM [Phillips 1998] Evolution pursuit Enhanced GA learning [Liu and Wechsler 2000a] Feature lines Point-to-line distance based [Li and Lu 1999] ICA ICA-based feature analysis [Bartlett et al. 1998] Other…”
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition (2002, background): “Face recognition depends heavily on the particular choice of features used by the classifier [22], [23].”
Object detection using feature subset selection (2004, background): “It is well known that the projection coeFcients de1ne a compact image representation and that a given image can be reconstructed from its projection coeFcients and the eigenvectors (i.e., basis).”
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition (2007, neutral): “Neural networks [25] have also been used for holistic face recognition.”
Face recognition from a single image per person: A survey (2006, methodology): “(3) Both evolution pursuit [34] and more recent Laplacianfaces [35] depend on large number of training samples per person to reliably estimate the so-needed low-dimensional manifold.”