Könyv Face Recognition & Principal Component Analysis Method Liton Chandra Paul

Face Recognition & Principal Component Analysis Method

Algorithm, Simulation & Discussion

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
13 432 Ft
This book mainly addresses the building of face recognition system and Principal Component Analysis...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2013
oldal
80
EAN
9783659461453
Enbook ID
02201775
Súly
137
Méretek
150 x 220 x 5

Teljes leírás

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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