During the ear detection described in Chapter 3, the optimization procedure drives the initial global registration towards the ear helix and anti-helix parts, which results in the one-to-one correspondence of the ear helix and anti-helix between the reference ear shape model and the input image. We propose to match 3D ears using the ear helix/antihelix representation [1]. First the correspondence of ear helix and antihelix parts (available from the ear detection algorithm) between every gallery-probe ear pair is established and it is used to compute the initial rigid transformation. Then this transformation is applied to randomly selected control points of the hypothesized gallery ear in the database. A modified iterative closest point (ICP) algorithm is run to improve the transformation which brings the gallery ear and probe ear into the best alignment, for every gallery-probe pair. The root mean square (RMS) registration error is used as the matching error criterion. The subject in the gallery with the minimum RMS error is declared as the recognized person in the probe image.
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Keywords
- Root Mean Square
- Equal Error Rate
- False Acceptance Rate
- Root Mean Square Distance
- Minimum Root Mean Square Error
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© 2008 Springer-Verlag London Limited
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(2008). Recognizing 3D Ears Using Ear Helix/Anti-Helix. In: Human Ear Recognition by Computer. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-129-9_4
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DOI: https://doi.org/10.1007/978-1-84800-129-9_4
Publisher Name: Springer, London
Print ISBN: 978-1-84800-128-2
Online ISBN: 978-1-84800-129-9
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