Fast face localization and verification

J.Matas, K.Johnson,J.Kittler

Presented by: Dong Xie

Introduction

Personal identification (authentication, verification of identity) – security applications.

Identification vs. Recognition– Small number of reference images vs. larger

database– Near real-time vs. w/o time constraint– Previously unseen person vs. image from

training database

In this article…

They propose an identification method based on optimized robust correlation.– An integrated approach: localization,

normalization as well as identification is achieved simultaneously.

– To that end, a robust form of correlation is evaluated inside an optimization loop.

– Random sampling to speed up evaluation of the cost function inside the optimization loop.

Optimized robust correlation… Objective: find the global extremum in a multi-

dimensional search space that corresponds to the best match between a pair of images

1. Score function: A combined score function.

2. Optimization method: – Each iteration, the transformation between reference

and test image is perturbed by adding a random vector drawn from an exponential distribution

– New transformation is accepted only if score was increased.

3. Random sampling

M2VTS Multi-modal Database: 5 ‘shots’/person over a period of several weeks

Example of output

3a-d SuccessfulSe-h Failed

High Score imposter test

Performance of the optimized robust correlation

•Equal Error Rate(EER): (a)search method.(b)number of test images used•Near Real time (0.24s/single identification):

(c) search method(client test) (d) client and imposter.

EER for Optimized Robust Correlation(6b):4.8% - single, randomly chosen 3.1% - sequence of test images

Conclusion…

A fast face localization and verification based on a robust form of correlation.

Optimization: random sampling speed the evaluation of correlation 25 times real time.

Recognition: Optimized Robust Correlation outperformed the two standard techniques.

Questions?