1. 3D Face Recognition Using Range Images
Final Presentation
Joonsoo Lee
5/03/05

2. Introduction Face Recognition Motivation Objective
Develop an automatic system which can recognize the human face as humans do
Motivation
Growing importance of security systems
Advance of image capture technology
Objective
To increase the recognition rate
To keep the computational complexity low

3. Background Range Image Previous Approach Image with depth information
Invariant to the change of illumination & color
Simple representation of 3D information
Previous Approach
Geometrical Approach: Principal Curvature [Gordon (1991)], Spherical Correlation [Tanaka & Ikeda (1998)]
Statistical Approach: Eigenface [Achermann et al. (1997)], Optimal Linear Component [Liu et al. (2004)]

4. Approach Pre-processing Feature Extraction 3D Mesh Image
3D coordinate & texture information
Range Image
Depth information extracted from 3D mesh
Normalized Image
Range image normalized by nose position
Maximum Curvature
PCA
Feature 1
Range Image
Curvature Analysis
Minimum Curvature
PCA
Feature 2
PCA: Principal Component Analysis

5. Curvature Analysis Curvature Calculation Normal Curvature (max, min)
Estimation of partial derivatives [Besl & Jain, 1986]

6. Result Database Recognition Rate Frontal & Neutral Expression
Various Expressions : poor performance
Range Image
Curvature Map
Recognition Rate
73.77 %
78.69 %

7. Conclusion & Future Work
Curvature information can play an important role in the face recognition problem
It still cannot handle various facial expressions
Future Work
Different kinds of curvature information will be utilized to find the best
Find the elements affected by the change of facial expressions