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Ethnicity prediction and classification from iris texture patterns: A survey on recent advances

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dc.contributor.author Mabuza-Hocquet, Gugulethu P
dc.contributor.author Nelwamondo, Fulufhelo V
dc.contributor.author Marwala, T
dc.date.accessioned 2017-08-22T13:09:09Z
dc.date.available 2017-08-22T13:09:09Z
dc.date.issued 2017-03
dc.identifier.citation Mabuza-Hocquet, G., Nelwamondo, F. and Marwala, T. 2017. Ethnicity prediction and classification from iris texture patterns: A survey on recent advances. 2016 International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, USA, 15-17 Dec 2016, p. 818-823. DOI: 10.1109/CSCI.2016.0159 en_US
dc.identifier.isbn 978-1-5090-5510-4
dc.identifier.uri DOI: 10.1109/CSCI.2016.0159
dc.identifier.uri http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7881452
dc.identifier.uri http://hdl.handle.net/10204/9460
dc.description Copyright: 2016 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website. en_US
dc.description.abstract The prediction and classification of ethnicity based on iris texture patterns using image processing, artificial intelligence and computer vision techniques is still a recent topic in iris biometrics. While the large body of knowledge and research in iris biometrics has been focused on using iris features for individual recognition and verification, very little work has been focused on using iris texture patterns to determine other demographic attributes of individuals such as ethnicity and gender. The aim of this paper is to consolidate most of the work done by researchers, through delving and comparing the different techniques, algorithms and results achieved over the last decade. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;19272
dc.subject Iris texture patterns en_US
dc.subject Iris ethnic classification en_US
dc.subject Iris feature extraction en_US
dc.subject Iris textons en_US
dc.title Ethnicity prediction and classification from iris texture patterns: A survey on recent advances en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Mabuza-Hocquet, G., Nelwamondo, F. V., & Marwala, T. (2017). Ethnicity prediction and classification from iris texture patterns: A survey on recent advances. IEEE. http://hdl.handle.net/10204/9460 en_ZA
dc.identifier.chicagocitation Mabuza-Hocquet, Gugulethu, Fulufhelo V Nelwamondo, and T Marwala. "Ethnicity prediction and classification from iris texture patterns: A survey on recent advances." (2017): http://hdl.handle.net/10204/9460 en_ZA
dc.identifier.vancouvercitation Mabuza-Hocquet G, Nelwamondo FV, Marwala T, Ethnicity prediction and classification from iris texture patterns: A survey on recent advances; IEEE; 2017. http://hdl.handle.net/10204/9460 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Mabuza-Hocquet, Gugulethu AU - Nelwamondo, Fulufhelo V AU - Marwala, T AB - The prediction and classification of ethnicity based on iris texture patterns using image processing, artificial intelligence and computer vision techniques is still a recent topic in iris biometrics. While the large body of knowledge and research in iris biometrics has been focused on using iris features for individual recognition and verification, very little work has been focused on using iris texture patterns to determine other demographic attributes of individuals such as ethnicity and gender. The aim of this paper is to consolidate most of the work done by researchers, through delving and comparing the different techniques, algorithms and results achieved over the last decade. DA - 2017-03 DB - ResearchSpace DP - CSIR KW - Iris texture patterns KW - Iris ethnic classification KW - Iris feature extraction KW - Iris textons LK - https://researchspace.csir.co.za PY - 2017 SM - 978-1-5090-5510-4 T1 - Ethnicity prediction and classification from iris texture patterns: A survey on recent advances TI - Ethnicity prediction and classification from iris texture patterns: A survey on recent advances UR - http://hdl.handle.net/10204/9460 ER - en_ZA


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