19 September 2014 Ear biometric recognition using local texture descriptors
Amir Benzaoui, Abdenour Hadid, Abdelhani Boukrouche
Author Affiliations +
Abstract
Automated personal identification using the shape of the human ear is emerging as an appealing modality in biometric and forensic domains. This is mainly due to the fact that the ear pattern can provide rich and stable information to differentiate and recognize people. In the literature, there are many approaches and descriptors that achieve relatively good results in constrained environments. The recognition performance tends, however, to significantly decrease under illumination variation, pose variation, and partial occlusion. In this work, we investigate the use of local texture descriptors, namely local binary patterns, local phase quantization, and binarized statistical image features for robust human identification from two-dimensional ear imaging. In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proven to be more effective in real-world conditions. Our extensive experimental results on the benchmarks IIT Delhi-1, IIT Delhi-2, and USTB ear databases show that local texture features in general and BSIF in particular provide a significant performance improvement compared to the state-of-the-art.
© 2014 SPIE and IS&T 0091-3286/2014/$25.00 © 2014 SPIE and IS&T
Amir Benzaoui, Abdenour Hadid, and Abdelhani Boukrouche "Ear biometric recognition using local texture descriptors," Journal of Electronic Imaging 23(5), 053008 (19 September 2014). https://doi.org/10.1117/1.JEI.23.5.053008
Published: 19 September 2014
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CITATIONS
Cited by 72 scholarly publications.
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KEYWORDS
Ear

Databases

Biometrics

Image filtering

Feature extraction

Linear filtering

Binary data

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