1. Fast face localization and verification J.Matas, K.Johnson,J.Kittler Presented by: Dong Xie

2. 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

3. 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.

4. 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

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

6. Example of output 3a-dSuccessful Se-hFailed

7. High Score imposter test

8. 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.

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

10. 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.

11. Questions?