DocumentCode
2859573
Title
Empirical Evaluation of Advanced Ear Biometrics
Author
Ping Yan ; Bowyer, KevinW.
Author_Institution
University of Notre Dame
fYear
2005
fDate
25-25 June 2005
Firstpage
41
Lastpage
41
Abstract
We present results of the largest experimental investigation of ear biometrics to date. Approaches considered include a PCA ("eigen-ear") approach with 2D intensity images, achieving 63.8% rank-one recognition; a PCA approach with range images, achieving 55.3% Hausdorff matching of edge images from range images, achieving 67.5% and ICP matching of the 3D data, achieving 98.7%. ICP based matching not only achieves the best performance, but also shows good scalability with size of dataset. The data set used represents over 300 persons, each with images acquired on at least two different dates. In addition, the ICP-based approach is further applied on an expanded data set of 404 subjects, and achieves 97.5% rank one recognition rate. In order to test the robustness and variability of ear biometrics, ear symmetry is also investigated. In our experiments around 90% of people’s right ear and left ear are symmetric.
Keywords
Bayesian methods; Biometrics; Computer science; Ear; Image recognition; Iterative closest point algorithm; Principal component analysis; Scalability; Shape; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on
Conference_Location
San Diego, CA, USA
ISSN
1063-6919
Print_ISBN
0-7695-2372-2
Type
conf
DOI
10.1109/CVPR.2005.450
Filename
1565339
Link To Document