Spatial approach to pose variations in face verification

Authors:
Licesio J. Rodríguez-Aragón;Ángel Serrano;Cristina Conde;Enrique Cabello
Affiliations:
Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

Citing 10
Cited 0

Support-Vector Networks

Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical

Advances in kernel methods
Membership authentication in the dynamic group by face classification using SVM ensemble

Pattern Recognition Letters
Growing Gaussian Mixture Models for Pose Invariant Face Recognition

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Model Based Face Recognition

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Face Authentication Test on the BANCA Database

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA

Pattern Recognition Letters
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spatial dimension reduction methods called Two Dimensional PCA and Two Dimensional LDA have recently been presented. These variations of traditional PCA and LDA consider images as 2D matrices instead of 1D vectors. The robustness to pose variations of these advances at verification tasks, using SVM as classification algorithm, is here shown. The new methods endowed with a classification strategy of SVMs, seriously improve, specially for pose variations, the results achieved by the traditional classification of PCA and SVM.