A survey of Face Recognition A survey of Face Recognition TechnologyTechnology

A survey of Face Recognition A survey of Face Recognition TechnologyTechnology

Wei-Yang LinWei-Yang LinMay 07, 2003May 07, 2003

Road Map• Introduction• Challenge in Face Recognition

– variation in pose– Variation in illumination

• Some recently works in FRT• Discussion

Introduction• FRT is a research area spanning

several disciplines.• Depending on the specific

application, FRT has different level of difficulty.

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

Illumination variation• Images of the same face appear

differently due to the change in lighting

• Naive Solution:– discarding the first few eigenfaces

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

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.

SFS results

Illumination cone• Illumination cone is a subspace

covers the variation in illumination.

Basis images

Synthetic images

Linear Object Class• How can we

recognize a face under different pose or expression when only one picture is given?

Linear Object Class

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

Elastic Bunch Graph• use Gabor wavelet

transform to extract face features so that the recognition performance can be invariant to the variation in poses.

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.

ComparisonsInput Database

(images / person)

Applicable to existing image database

SFS One image One image All

Illumination cone

One image Several images Some

Linear object One image Several images Some

Curvature-based Range data Range data No

Elastic Bunch Graph

One image Several images Some

2D-3D combined Range data + one image

Range data + several images

No