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3D Face Recognition Using Range Images. Final Presentation Joonsoo Lee 5/03/05. Introduction. Face Recognition 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
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3D Face Recognition Using Range Images Final Presentation Joonsoo Lee 5/03/05
Introduction • Face Recognition • 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 • 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 Feature 1 PCA 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
Conclusion & Future Work • Conclusion • 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