Abstract
The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.
MeSH terms
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Biometric Identification / instrumentation
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Biometric Identification / methods*
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Datasets as Topic
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Ear / anatomy & histology*
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Humans
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Image Processing, Computer-Assisted / methods
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Image Processing, Computer-Assisted / statistics & numerical data*
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Imaging, Three-Dimensional / statistics & numerical data*
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Lasers
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Machine Learning*
Grants and funding
The work is partially supported by the GRF fund from the HKSAR Government, the central fund from Hong Kong Polytechnic University, the NSFC fund (61332011, 61272292, 61271344), Shenzhen Fundamental Research fund (JCYJ20150403161923528), and Key Laboratory of Network Oriented Intelligent Computation, Shenzhen, China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.