Face authentication using one-class support vector machines

Authors:
Manuele Bicego;Enrico Grosso;Massimo Tistarelli
Affiliations:
DEIR, University of Sassari, Sassari, Italy;DEIR, University of Sassari, Sassari, Italy;DAP, University of Sassari, Alghero, SS, Italy
Venue:
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Year:
2005

Citing 7
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Support vector domain description

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Support vector clustering

The Journal of Machine Learning Research
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Face detection using one-class-based support vectors

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition

Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new method for personal identity verification based the analysis of face images applying One Class Support Vector Machines. This is a recently introduced kernel method to build a unary classifier to be trained by using only positive examples, avoiding the sensible choice of the impostor set typical of standard binary Support Vector Machines. The features of this classifier and the application to face-based identity verification are described and an implementation presented. Several experiments have been performed on both standard and proprietary databases. The tests performed, also in comparison with a standard classifier built on Support Vector Machines, clearly show the potential of the proposed approach.