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Human Ear Recognition in 3D
Abstract
Human ear is a new class of relatively stable biometrics that has drawn researchers' attention recently. In this paper, we propose a complete human recognition system using 3D ear biometrics. The system consists of 3D ear detection, 3D ear identification, and 3D ear verification. For ear detection, we propose a new approach which uses a single reference 3D ear shape model and locates the ear helix and the antihelix parts in registered 2D color and 3D range images. For ear identification and verification using range images, two new representations are proposed. These include the ear helix/antihelix representation obtained from the detection algorithm and the local surface patch (LSP) representation computed at feature points. A local surface descriptor is characterized by a centroid, a local surface type, and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values versus the angles between the normal of reference feature point and that of its neighbors. Both shape representations are used to estimate the initial rigid transformation between a gallery-probe pair. This transformation is applied to selected locations of ears in the gallery set and a modified Iterative Closest Point (ICP) algorithm is used to iteratively refine the transformation to bring the gallery ear and probe ear into the best alignment in the sense of the least root mean square error. The experimental results on the UCR data set of 155 subjects with 902 images under pose vari…
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“Therefore, much like fingerprint and face recognition (Smeets et al. 2010(Smeets et al. , 2012)), ear recognition (Chen & Bhanu, 2007;Yan & Bowyer, 2007;Abaza et al. 2013) is an active field of research in biometrics (biometric authentication).”
From An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization (2015) · cites this paper as Chen & Bhanu, 2007
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Cited by
Sorted by citations
A review of biometric technology along with trends and prospects
Unar, J. A., Seng, W. C., & Abbasi, A. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.01.016 · Cited by 404
Claes, P., Reijniers, J., Shriver, M. D., Snyders, J., Suetens, P., Nielandt, J., De Tré, G., & Vandermeulen, D. (2015) · Journal of Anatomy · doi:10.1111/joa.12252 · Cited by 32
Metadata
- Authors
- Chen, H., & Bhanu, B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-04
- Volume
- 29
- Issue
- 4
- Pages
- 718-737
- ISSN
- 0162-8828
- 2160-9292
- Cited by
- 240
- References
- 0
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