A 3D ear recognition method based on auricle structural feature
Kai Wang, Zhichun Mu · 2013
The performances of most existing 3D ear recognition methods are degraded sharply by pose variation. In this paper, a 3D ear representation called 3D auricle structural feature(3DASF) and the corresponding pose robust 3D ear recognition method is presented. By measuring the surface characteristics through Surface Variation, 3DASF that contains ear key physiological structure is extracted. Then 3DASF corresponding points are used to implement iterative closest point(ICP) algorithm to coarse align gallery-probe ear pairs. Finally, fine alignment is performed to obtain the alignment errors for identity recognition. Experimental results conducted on University of Notre Dame(UND) biometric datasets collection F and collection G outperform the state-of-the-art 3D ear recognitions based on ICP. The results also demonstrate that the proposed method is more robust to pose variation than the state-of-the-art.