Abstract
The available face views in the training set are mostly limited. In this paper, we present a view interpolation method using nonlinear B-spline on face manifolds. Two models, the inner-outer ellipse model and the moment of inertia model, are developed to estimate the pose orientation. We use the limited view-pose face images to form the pose eigen space. Then, based on these nonlinear manifolds we form a B-spline for each individual. Identification is to compute the shortest Euclidean distance from a given test view to the nearest point within one of these B-splines. Once the test view is classified as a familiar individual in the training set, not only can the individual be identified, but also the pose angle can be estimated. Experimental results show that B-spline interpolation can achieve a recognition rate of 95%.
Original language | English (US) |
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Pages (from-to) | 189-204 |
Number of pages | 16 |
Journal | Information sciences |
Volume | 169 |
Issue number | 3-4 |
DOIs | |
State | Published - Feb 1 2005 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence
Keywords
- Face recognition
- Pattern recognition
- Pose estimation
- Principal component analysis
- Spline interpolation