Ear biometric recognition using local texture descriptors

被引:59
作者
Benzaoui, Amir [1 ]
Hadid, Abdenour [2 ]
Boukrouche, Abdelhani [1 ]
机构
[1] Univ May 08th 1945, Lab Inverse Problems Modeling Informat & Syst, Dept Elect & Telecommun, Guelma 24000, Algeria
[2] Univ Oulu, Ctr Machine Vis Res, Dept Comp Sci & Engn, Oulu 90014, Finland
关键词
ear biometric identification; image feature extraction; texture analysis; local binary patterns; local phase quantization; binarized statistical image features; FEATURE-EXTRACTION; CLASSIFICATION; ALGORITHMS; ROTATION; SCALE; SHAPE;
D O I
10.1117/1.JEI.23.5.053008
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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. (C) 2014 SPIE and IS&T
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页数:12
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