1. A survey of Face Recognition Technology Wei-Yang Lin May 07, 2003

2. Road Map Introduction Challenge in Face Recognition –variation in pose –Variation in illumination Some recently works in FRT Discussion

3. Introduction FRT is a research area spanning several disciplines. Depending on the specific application, FRT has different level of difficulty.

4. Challenges in FRT The recent FERET test has revealed that there are at least two major challenges: –The illumination variation problem –The pose variation problem

5. Illumination variation Images of the same face appear differently due to the change in lighting Naive Solution: –discarding the first few eigenfaces

6. Pose Variation Basically, the existing solution can be divided into three types: – multiple images in both training stage and recognition stage –multiple images in training stage, but only one image in recognition stage –single image based methods

7. Shape-from-Shading The basic idea of SFS is to infer the 3D surface of object from the shading information in image. Lambertian model has been used extensively in computer vision community for the SFS problem.

8. SFS results

9. Illumination cone Illumination cone is a subspace covers the variation in illumination. Basis images Synthetic images

10. Linear Object Class How can we recognize a face under different pose or expression when only one picture is given?

11. Linear Object Class

12. Curvature-based FRT Use the curvature of surface to perform face recognition This is a great idea since the value of curvature at a point on the surface is invariant under the variation of viewpoint and illumination

13. Elastic Bunch Graph use Gabor wavelet transform to extract face features so that the recognition performance can be invariant to the variation in poses.

14. 2D-3D Face Recognition Almost all existing systems rely on either 2D images or 3D range data. 3D shape can compensate for the lack of depth information in 2D image. Therefore, integrating 2D and 3D information will be a possible way to improve the recognition performance.

15. Comparisons InputDatabase (images / person) Applicable to existing image database SFSOne image All Illumination coneOne imageSeveral imagesSome Linear objectOne imageSeveral imagesSome Curvature-basedRange data No Elastic Bunch Graph One imageSeveral imagesSome 2D-3D combinedRange data + one image Range data + several images No