Face Surface Recognition from Surface Curvature
| Project/Area Number |
08680416
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | Ritsumeikan University |
|---|
Principal Investigator |
TANAKA Hiromi T. RITSUMEIKAN UNIV., SCIENCE & ENG.PROFESS, 理工学部, 教授 (10268154)
|
|---|
| Project Period (FY) |
1996 – 1997
|
| Project Status |
Completed (Fiscal Year 1997)
|
| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 1997: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1996: ¥1,400,000 (Direct Cost: ¥1,400,000)
|
|---|
| Keywords | Face Pecognition / Surface Curvature / Range Image Aralysis / Spherical Correlation / Differential Geometry / 3次元曲率を用いた顔認識 / 距離画像から形状特徴抽出 / 3D曲率・方向の拡張ガウス像 / 拡張ガウス像からの特徴抽出 / 3次元曲率の識別能力評価 / 顔認識 / 微分幾何学-3次元曲率 / 距離画像の形状特徴抽出 / 3次元曲率の拡張ガウス像表現 |
|---|
| Research Abstract |
Surface curvatures such as Gaussian, mean and principal curvatures are intrinsic surface properties and have playd important roles in curved surface analysis. However, only their signs are reliably used in classifying and identifying surfaces.
In this paper, we treat face recognition problem as 3D shape recognition problem of free-form curved surfaces and present an approach based on the analysis of two principal curvatures, max and minimum curvatures and their directions. The approach is based on simple correlation-based matching algorithm which does not require either face feature extraction or surface segmentation.
Each face in both input images and the model database, is represented as an Extended Gaussian Image(EGI), constructed by mapping principal (max and minimum) curvatures and their directions onto the unit sphere, estimated at each surface points. Individual face is then recognized by evaluating the similarities among others by using Fisher's spherical correlation.
The method is tested for its simplicity and robustness and successively implemented for each of 37 face range images from NRCC (National Research Council Canada) 3D image data files. It turned out that the method is quite simple, efficient and robust to distractions such glasses, facial hair or hair style. These represent important advantages over conventional method based 2D features such as eye, mouth patterns. Results shows that shape information from surface curvatures provides vital cues in distinguishing and identifying such fine surface structure as human faces.
|
Report
(3 results)
Research Products
(9 results)
