Extraction and fusion of partial face features for cancelable identity verification

Authors:
Beom-Seok Oh;Kar-Ann Toh;Kwontaeg Choi;Andrew Beng Jin Teoh;Jaihie Kim
Affiliations:
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;Department of Computer Science, Yonsei University, Seoul, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
Venue:
Pattern Recognition
Year:
2012

Citing 23
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Database-friendly random projections

PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
An improved face recognition technique based on modular PCA approach

Pattern Recognition Letters
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier

IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing security and privacy in biometrics-based authentication systems

IBM Systems Journal - End-to-end security
On the Euclidean Distance of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
2005 Special Issue: Generalized 2D principal component analysis for face image representation and recognition

Neural Networks - 2005 Special issue: IJCNN 2005
Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Diagonal principal component analysis for face recognition

Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Biometric scores fusion based on total error rate minimization

Pattern Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Between Classification-Error Approximation and Weighted Least-Squares Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation

ICB '07 Proceedings of the international conference on Advances in Biometrics
Letters: (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition

Neurocomputing
Score normalization in multimodal biometric systems

Pattern Recognition
A performance driven methodology for cancelable face templates generation

Pattern Recognition
Cancelable Biometrics Realization With Multispace Random Projections

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Horizontal and vertical 2DPCA based discriminant analysis for face verification using the FRGC version 2 database

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose to extract localized random features directly from partial face image matrix for cancelable identity verification. Essentially, the extracted random features consist of compressed horizontal and vertical facial information obtained from a structured projection of the raw face images. For template security reason, the face appearance information is concealed via averaging several templates over different transformations. The match score outputs of these cancelable templates are then fused through a total error rate minimization. Extensive experiments were carried out to evaluate and benchmark the performance of the proposed method based on the AR, FERET, ORL, Sheffield and BERC databases. Our empirical results show encouraging performances in terms of verification accuracy as well as satisfying four cancelable biometric properties.