Face Recognition Using Principal Component Analysis Method

of eigenvectors is used to transform training and test images into face space using

The system is composed of Preprocessing, Principal Component Analysis, Viola Jones Algorithm for Feature Extraction and Euclidean classifier to find the

This chapter describes the tasks of face recognition systems which include data acquisition, face detection, preprocessing, features extraction, features matching and

In the proposed method, the frontal face images are gabor filtered and the vector representation of these filtered face image is reduced using PCA.. The reduced principle

In this approach we use the concept of principal component analysis (PCA) to transform a face image onto a lower dimensional subspace that consists eigenfaces.. PCA [5] [6]

Firstly, the feature vector of the face image is extracted then; genetic programming is applied on this vector to extract a mathematical formula for face

In principle component analysis (PCA) based face recognition technique, the 2D face image matrices must be previously transformed in to one dimensional image

In the context of online single- image face recognition, each training image is transformed into its feature vector by using the basis vectors from the MPEG-7 face descriptor and