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Representation of pose invariant face images using SIFT descriptors

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dc.contributor.author Mokoena, Nthabiseng ME
dc.contributor.author Nair, Kishor K
dc.date.accessioned 2019-03-05T10:03:01Z
dc.date.available 2019-03-05T10:03:01Z
dc.date.issued 2018-08
dc.identifier.citation Mokoena, N.M.E. and Nair, K.K. 2018. Representation of pose invariant face images using SIFT descriptors. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, South Africa, 6-7 August 2018 en_US
dc.identifier.isbn 978-1-5386-3060-0
dc.identifier.isbn 978-1-5386-3061-7
dc.identifier.uri https://ieeexplore.ieee.org/document/8465462
dc.identifier.uri DOI: 10.1109/ICABCD.2018.8465462
dc.identifier.uri http://hdl.handle.net/10204/10741
dc.description Copyright: 2018 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published paper. For access to the published item, please consult the publisher's website: https://ieeexplore.ieee.org/document/8465462 en_US
dc.description.abstract The choice of a face database should solemnly depend on the problem to be solved. In this research work, we use the Face Recognition Technology (FERET) database to address the challenge of face pose variations. The Scale Invariant Feature Transform (SIFT) is used to represent these face images in the database. SIFT has been proven to be a robust and a powerful method for general object detection in the past years. This method is now popular in the field of face recognition for purposes of extracting key points which are scale and orientation invariant from the face image. This work demonstrates that through extracting SIFT features from different face image patches and at different sigma s values, a face pose can be classified towards better pose invariant face recognition. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;21364
dc.subject Face recognition en_US
dc.subject Machine Learning algorithms en_US
dc.subject Pose-invariant face classification en_US
dc.subject Scale Invariant Feature Transform en_US
dc.subject SIFT en_US
dc.title Representation of pose invariant face images using SIFT descriptors en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Mokoena, N. M., & Nair, K. K. (2018). Representation of pose invariant face images using SIFT descriptors. IEEE. http://hdl.handle.net/10204/10741 en_ZA
dc.identifier.chicagocitation Mokoena, Nthabiseng ME, and Kishor K Nair. "Representation of pose invariant face images using SIFT descriptors." (2018): http://hdl.handle.net/10204/10741 en_ZA
dc.identifier.vancouvercitation Mokoena NM, Nair KK, Representation of pose invariant face images using SIFT descriptors; IEEE; 2018. http://hdl.handle.net/10204/10741 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Mokoena, Nthabiseng ME AU - Nair, Kishor K AB - The choice of a face database should solemnly depend on the problem to be solved. In this research work, we use the Face Recognition Technology (FERET) database to address the challenge of face pose variations. The Scale Invariant Feature Transform (SIFT) is used to represent these face images in the database. SIFT has been proven to be a robust and a powerful method for general object detection in the past years. This method is now popular in the field of face recognition for purposes of extracting key points which are scale and orientation invariant from the face image. This work demonstrates that through extracting SIFT features from different face image patches and at different sigma s values, a face pose can be classified towards better pose invariant face recognition. DA - 2018-08 DB - ResearchSpace DP - CSIR KW - Face recognition KW - Machine Learning algorithms KW - Pose-invariant face classification KW - Scale Invariant Feature Transform KW - SIFT LK - https://researchspace.csir.co.za PY - 2018 SM - 978-1-5386-3060-0 SM - 978-1-5386-3061-7 T1 - Representation of pose invariant face images using SIFT descriptors TI - Representation of pose invariant face images using SIFT descriptors UR - http://hdl.handle.net/10204/10741 ER - en_ZA


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