Eigenface: Exploring the Depths of Visual Recognition with Eigenface
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An eigenface is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification. The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Eigenface
Chapter 2: Principal component analysis
Chapter 3: Singular value decomposition
Chapter 4: Eigenvalues and eigenvectors
Chapter 5: Eigendecomposition of a matrix
Chapter 6: Kernel principal component analysis
Chapter 7: Matrix analysis
Chapter 8: Linear dynamical system
Chapter 9: Multivariate normal distribution
Chapter 10: Modes of variation
(II) Answering the public top questions about eigenface.
(III) Real world examples for the usage of eigenface in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Eigenface.
