Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications

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• 作者：Pattabhi Ramaiah Nalla ; Krishna Mohan Chalavadi
• 关键词：De ; duplication ; Biometrics ; Iris fibers ; Iris classification ; Iris adjudication ; Sparse representation ; On ; line dictionary learning
• 刊名：SpringerPlus
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：4
• 期：1
• 全文大小：3142KB
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• 作者单位：Pattabhi Ramaiah Nalla (1)
  Krishna Mohan Chalavadi (1)
  
  1. Department of Computer Science and Engineering, Indian Institute of Technology Hyderabad ODF Estate, Medak, Telangana, 502205, India
  
• 刊物类别：Science, general;
• 刊物主题：Science, general;
• 出版者：Springer International Publishing
• ISSN：2193-1801

文摘

De-duplication of biometrics is not scalable when the number of people to be enrolled into the biometric system runs into billions, while creating a unique identity for every person. In this paper, we propose an iris classification based on sparse representation of log-gabor wavelet features using on-line dictionary learning (ODL) for large-scale de-duplication applications. Three different iris classes based on iris fiber structures, namely, stream, flower, jewel and shaker, are used for faster retrieval of identities. Also, an iris adjudication process is illustrated by comparing the matched iris-pair images side-by-side to make the decision on the identification score using color coding. Iris classification and adjudication are included in iris de-duplication architecture to speed-up the identification process and to reduce the identification errors. The efficacy of the proposed classification approach is demonstrated on the standard iris database, UPOL. Keywords De-duplication Biometrics Iris fibers Iris classification Iris adjudication Sparse representation On-line dictionary learning