Kniha Face Recognition & Principal Component Analysis Method Liton Chandra Paul

Face Recognition & Principal Component Analysis Method

Algorithm, Simulation & Discussion

Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: Skladem u dodavatele
Odesíláme za 5-8 dnů
789
This book mainly addresses the building of face recognition system and Principal Component Analysis...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2013
Stránek
80
EAN
9783659461453
Enbook ID
02201775
Hmotnost
137
Rozměry
150 x 220 x 5

Kompletní popis

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.

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