Abstract
We propose a system for time-efficient 3D ear biometrics. The system is composed of two primary components, namely: (1) an ear shape-based index; and (2) categorization using the index. We built an index tree by using the shape feature computed from measures of circularity, rectangularity, ellipticity, and triangularity, based on ear segmentation results and then perform a nearest neighbor search to obtain a gallery of ear images that are closest in shape to the probe subjects. For the categorization component, separate index trees are built out of the gallery of ear images by using a reduced depth feature space for each image. We utilize an indexing technique to perform a range query in a reduced depth feature space for ears that are closest in shape to the probe subject. Experiments on the benchmark database demonstrate that the proposed approach is more efficient compared to the state-of-the-art 3D ear biometric system.
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Zhu, Q., Mu, Z. (2018). An Efficient 3D Ear Recognition System Based on Indexing. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_54
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DOI: https://doi.org/10.1007/978-3-319-97909-0_54
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