A Study on 2D PCA Face Recognition Method

Face Recognition using PCA, Deep Face Method

The performance process of face recognition involves the inspection study of facial features in an image, recognizing those features and comparing them to one of the many faces in the database. There are many algorithms capable of performing face recognition; such as: Principal Component Analysis, Discrete Cosine Transform, 3D recognition methods, Gabor Wavelets method etc. There were many issu...

Canonical Correlated PCA method For Face Recognition

Face Recognition using Eigenfaces , PCA and Supprot Vector Machines

This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...

Lophoscopic Pca: a Novel Method for Face Recognition

In this paper, a new technique called Lophoscopic PCA is developed for recognition of partially occluded faces and faces with strong facial expression variations. As opposed to the PCA or the Eigenfeatures approaches, this method does not try to solve the face recognition problem neither from a holistic, nor a feature perspective; in fact it studies the problem from a near or pseudo-holistic pe...

PCA and kernel PCA using polynomial filtering: a case study on face recognition∗

Principal component analysis (PCA) is an extensively used dimensionality reduction technique, with important applications in many fields such as pattern recognition, computer vision and statistics. It employs the eigenvectors of the covariance matrix of the data to project it on a lower dimensional subspace. Kernel PCA, a generalized version of PCA, performs PCA implicitly in a nonlinearly tran...