Multiview Face Recognition based on Canonical Correlated PCA

SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis

Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is u...

Robust multi-camera view face recognition

This paper presents multi-appearance fusion of Principal Component Analysis (PCA) and generalization of Linear Discriminant Analysis (LDA) for multi-camera view offline face recognition (verification) system. The generalization of LDA has been extended to establish correlations between the face classes in the transformed representation and this is called canonical covariate. The proposed system...

2D Face Recognition Based on PCA & Comparison of Manhattan Distance, Euclidean Distance & Chebychev Distance

This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) Applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component...

Canonical Correlated PCA method For Face Recognition

تشخیص چهره با استفاده از PCA و فیلتر گابور

Methods for face recognition which are based on face structure are among techniques without supervision and produce unfavorable results in the presence of linear changes in images. PCA is a linear transform and a powerful tool for data analysis but does not produce good results for face recognition when there are non-linear changes resulting from changes in position, intensity and gesture in th...