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

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

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)]

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

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

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

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