3D Face Recognition Using Range Images
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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
3D Face Recognition Using Range Images
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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