Ear recognition via sparse coding of local features
An efficient scheme for human ear recognition is presented. This scheme comprises three main phases. First, the ear image is decomposed into a pyramid of progressively downgraded images, which allows the local patterns of the ear to be captured. Second, histograms of local features are extracted fro...
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| Published in | Journal of electronic imaging Vol. 27; no. 1; p. 013007 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Society of Photo-Optical Instrumentation Engineers
01.01.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1017-9909 1560-229X |
| DOI | 10.1117/1.JEI.27.1.013007 |
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| Abstract | An efficient scheme for human ear recognition is presented. This scheme comprises three main phases. First, the ear image is decomposed into a pyramid of progressively downgraded images, which allows the local patterns of the ear to be captured. Second, histograms of local features are extracted from each image in the pyramid and then concatenated to shape one single descriptor of the image. Third, the procedure is finalized by using decision making based on sparse coding. Experiments conducted on two datasets, composed of 125 and 221 subjects, respectively, have demonstrated the efficiency of the proposed strategy as compared to various existing methods. For instance, scores of 96.27% and 96.93% have been obtained for the datasets, respectively. |
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| AbstractList | An efficient scheme for human ear recognition is presented. This scheme comprises three main phases. First, the ear image is decomposed into a pyramid of progressively downgraded images, which allows the local patterns of the ear to be captured. Second, histograms of local features are extracted from each image in the pyramid and then concatenated to shape one single descriptor of the image. Third, the procedure is finalized by using decision making based on sparse coding. Experiments conducted on two datasets, composed of 125 and 221 subjects, respectively, have demonstrated the efficiency of the proposed strategy as compared to various existing methods. For instance, scores of 96.27% and 96.93% have been obtained for the datasets, respectively. |
| Author | Ali, Taghreed Abdullah Mohammed Al Rahhal, Mohamad Mahmoud Bazi, Yakoub Al Zuair, Mansour Mekhalfi, Mohamed Lamine Rangarajan, Lalitha |
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| Keywords | local features biometrics ear recognition sparse coding |
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| References | Bustard, J. D.; Nixon, M. S. 2010; 40 Guermoui, M.; Melaab, D.; Mekhalfi, M. L. 2016; 55 Chan, T.-S.; Kumar, A. 2012; 33 Candes, E. J.; Tao, T. 2005; 51 Candès, E. J. 2008; 346 Yuan, L.; Chun Mu, Z. 2012; 33 Donoho, D. L. 2006; 52 Donoho, D. L. 2012; 58 Kumar, A.; Wu, C. 2012; 45 Chang, K. 2003; 25 Hurley, D. J.; Nixon, M. S.; Carter, J. N. 2002; 20 Sibai, F. N. 2013; 23 Araujo, L. C. F. 2005; 53 Ming, J. 2007; 15 Ngo, D. C. L.; Teoh, A. B. J.; Goh, A. 2006; 16 Nanni, L.; Lumini, A. 2009; 42 r3 r4 r6 r7 r8 r9 r10 r21 r20 r12 r23 r11 r22 r14 r13 r24 r16 r15 r18 Maltoni (r2) 2009 r17 Whitman (r5) 2015 r19 r1 |
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