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Applicants name:Danqing Yin

Proposed Topic:

3D Reconstruction for Face Recognition

Background:

Although over three decades of research efforts, face recognition is still a big

challenge in digital image processing. Face recognition based on two-dimensional

image is relatively mature, however, is still far from satisfactory among different pose,

illumination and expression. For example, when a person opens his jaw or changes

from neutral expression to smiling, face recognition based on two-dimensional image

is hard to meet our need. In order to deal with this problem, 3D reconstruction for

human face is new way to enhance the representation of face gallery. Photometric

stereo technologies is to deal with face recognition under different illumination

conditions. And depth image method is used to extract depth information of human

face for 3D reconstruction.

In this study, I propose to combine some of the methodologies mentioned above with

image based rendering( IBR) methods to better representation of 3D human face.

Database for 3D human face model will be built for later face matching.

Methodology:

3D reconstruction for human face based on IBRIn this section, IBR method is used for accurate 3D human face reconstruction.Because pure geometry method can only deal with rough 3D head model and cannot

accurately work on local features, like mouth or eyes, thus, IBR method can be used

to describe these local features by interpolating a set of images. Different samples

have different head models. Though capturing their neutral expressions, the local

features can be represented with IBR method. Then, database for 3D human face

model is built.

Human face animation parameters extractingDifferent from the first section, extracting human face animation parameters is not for

reconstructing human face model, but for reconstructing local face expression. By

using mesh models of images, different face expression can be described by different

mesh models and face animation parameters can be extracted from these mesh models.

Thus, when matching face images show some face expression, 3D human face model

can be refined long with these face animation parameters.

Face recognitionFirst, an appropriate head model is chosen from the database. Then, according to

matching images face animation parameters, the face expression can be described

through changing the mesh-model on the face. The similarity between the face model

and real face image is calculated later. If the results is within the error range, the face

recognition is done.

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Reference:

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Zicheng Liu, Ying Shan and Zhengyou Zhang, " Expressive Expression Mapping with

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