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A survey of Face Recognition Technology

A survey of Face Recognition Technology

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A survey of Face Recognition Technology

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  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