DocumentCode :
620357
Title :
A rotation invariant feature extraction for 3D ear recognition
Author :
Yiliang Li ; Zhichun Mu ; Hui Zeng
Author_Institution :
Sch. of Autom. & Electr. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
fYear :
2013
fDate :
25-27 May 2013
Firstpage :
3671
Lastpage :
3675
Abstract :
In this paper, we use a new method, which based on the spherical harmonics transform, to extract 3D ear rotation invariant feature from 3D ear data. And a 3D ear recognition system was built through matching ear spherical harmonics feature, which is rotation invariant in mathematical theory. Utilizing its rotation invariant property, the 3D ear recognition method could be strongly robust in pose variation. A Rank-1 recognition rate, achieved in experiment, is 96.4% on a data set of 415 subjects, and processing time also has been reduced, comparing to the well-known ICP algorithm.
Keywords :
ear; feature extraction; image matching; mathematical analysis; 3D ear data; 3D ear recognition; 3D ear rotation invariant feature extraction; ear spherical harmonics feature matching; mathematical theory; pose variation; rotation invariant property; spherical harmonics transform; Ear; Feature extraction; Harmonic analysis; Image recognition; Iterative closest point algorithm; Pattern recognition; Transforms; 3D ear recognition; Rotation invariant ear feature; Spherical harmonics transform; Spherical sampling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
Type :
conf
DOI :
10.1109/CCDC.2013.6561586
Filename :
6561586
Link To Document :
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