Biometric Face Recognition with Different Training and Testing Databases

Authors:
Joan Fabregas;Marcos Faundez-Zanuy
Affiliations:
Escola Universitària Politècnica de Mataró (Adscrita a la UPC), MATARO (BARCELONA), Spain 08303;Escola Universitària Politècnica de Mataró (Adscrita a la UPC), MATARO (BARCELONA), Spain 08303
Venue:
Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction
Year:
2008

Citing 11
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society

Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Guide to Biometrics

Guide to Biometrics
An efficient face verification method in a transformed domain

Pattern Recognition Letters
Eigenfaces for recognition

Journal of Cognitive Neuroscience
On the Relevance of Facial Expressions for Biometric Recognition

Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction
Performance evaluation in 1: 1 biometric engines

SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication

Quantified Score

Hi-index	0.00

Visualization

Abstract

Biometric face recognition presents a wide range of variability sources, such as make up, illumination, pose, facial expression, etc. Although some public available databases include these phenomena, it is a laboratory condition far away from real biometric system scenarios. In this paper we perform a set of experiments training and testing with different face databases in order to reduce the wide range of problems present in face images from different users (make up, facial expression, rotations, etc.). We use a novel dispersion matcher, which opposite to classical biometric systems, does not need to be trained with the whole set of users. It can recognize if two photos are of the same person, even if the photos of that person were not used in training the classifier.