Книга: Liton Chandra Paul and Abdulla al Suman «Face Recognition&Principal Component Analysis Method»
|
Производитель: "LAP Lambert Academic Publishing" This book mainly addresses the building of face recognition system and Principal Component Analysis (PCA) method in details. PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. These eigenvectors are obtained from covariance matrix of a training image set called as basis function. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a test image onto the subspace spanned by the eigenfaces and then classification is done by measuring Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system. Here, I used a training database of students of ETE-07 series, RUET, Rajshahi-6204, Bangladesh. ISBN:9783659461453 Издательство: "LAP Lambert Academic Publishing" (2013)
ISBN: 9783659461453 |
См. также в других словарях:
Principal component analysis — PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by… … Wikipedia
Linear discriminant analysis — (LDA) and the related Fisher s linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The… … Wikipedia
Eigenvalues and eigenvectors — For more specific information regarding the eigenvalues and eigenvectors of matrices, see Eigendecomposition of a matrix. In this shear mapping the red arrow changes direction but the blue arrow does not. Therefore the blue arrow is an… … Wikipedia
Eigenface — Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face… … Wikipedia
Constellation model — The constellation model is a probabilistic, generative model for category level object recognition in computer vision. Like other part based models, the constellation model attempts to represent an object class by a set of N parts under mutual… … Wikipedia
Histogram of oriented gradients — Histogram of Oriented Gradient descriptors, or HOG descriptors, are feature descriptors used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized… … Wikipedia
