Conference Paper

A rotation invariant feature extraction for 3D ear recognition

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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.

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... We found (28/48) articles in 3D ear recognition that attempted on using the large amount of information from 3D ear image structures (such as size, color and texture) for recognition system. The following are the various approaches and techniques used:  geometric feature extraction [208]- [211],  3D hybrid ear techniques [212]- [215]  iterative closest point algorithm [216]- [221],  Gaussian-weighted average of the mean curvature [222], [223],  sparse representation [224], [225],  scale invariant feature transform (SIFT) [226],  wavelets on geometry images [227],  structure from motion (SFM) and shape from shading (SFS) techniques [228],  shape-based interest point descriptor (SIP) [229],  removal of false mapped features of 3D shapes [230],  local histograms of surface types for feature extraction [231],  Local Salient Shape Feature [232],  spherical harmonics transform algorithm [233],  slice curve matching [234], and  combination of feature embedding [235]. ...
... We found (28/48) articles in 3D ear recognition that attempted on using the large amount of information from 3D ear image structures (such as size, color and texture) for recognition system. The following are the various approaches and techniques used:  geometric feature extraction [208]- [211],  3D hybrid ear techniques [212]- [215]  iterative closest point algorithm [216]- [221],  Gaussian-weighted average of the mean curvature [222], [223],  sparse representation [224], [225],  scale invariant feature transform (SIFT) [226],  wavelets on geometry images [227],  structure from motion (SFM) and shape from shading (SFS) techniques [228],  shape-based interest point descriptor (SIP) [229],  removal of false mapped features of 3D shapes [230],  local histograms of surface types for feature extraction [231],  Local Salient Shape Feature [232],  spherical harmonics transform algorithm [233],  slice curve matching [234], and  combination of feature embedding [235]. ...
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