Similarity rank correlation for face recognition under unenrolled pose

Authors:
Marco K. Müller;Alexander Heinrichs;Andreas H. J. Tewes;Achim Schäfer;Rolf P. Würtz
Affiliations:
Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany
Venue:
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Year:
2007

Citing 7
Cited 4

Numerical recipes in C: the art of scientific computing

Numerical recipes in C: the art of scientific computing
Face Recognition by Elastic Bunch Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture

IEEE Transactions on Computers
Pose-Invariant Face Recognition with Parametric Linear Subspaces

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Fitting a 3D Morphable Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds

The Journal of Machine Learning Research
A flexible object model for recognising and synthesising facial expressions

AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication

Face recognition across pose: A review

Pattern Recognition
Combining feature-and correspondence-based methods for visual object recognition

Neural Computation
Learning from Examples to Generalize over Pose and Illumination

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
2013 Special Issue: Learning invariant face recognition from examples

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face recognition systems have to deal with the problem that not all variations of all persons can be enrolled. Rather, the variations of most persons must be modeled. Explicit modeling of different poses is awkward and time consuming. Here, we present a subsystem that builds a model of pose variation by keeping a model database of persons in both poses, additionally to the gallery of clients known in only one pose. An identification or verification decision for probe images is made on the basis of the rank order of similarities with the model database. Identification achieves up to 100% recognition rate on 300 pairs of testing images with 45 degrees pose variation within the CAS-PEAL database, the equal error rate for verification reaches 0.5%.