Decision-level fusion for single-view gait recognition with various carrying and clothing conditions
Gait Recognition is one of the latest and attractive biometric techniques, due to its potential in identification of individuals at a distance, unobtrusively and even using low resolution images. In this paper we focus on single lateral view gait recognition with various carrying and clothing condit...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2017
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/8817 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513443673931776 |
|---|---|
| author | Al-Tayyan, Amer |
| author2 | Assaleh, Khaled Shanableh, Tamer |
| author2_role | author author |
| author_facet | Al-Tayyan, Amer Assaleh, Khaled Shanableh, Tamer |
| author_role | author |
| dc.creator.none.fl_str_mv | Al-Tayyan, Amer Assaleh, Khaled Shanableh, Tamer |
| dc.date.none.fl_str_mv | 2017-05-01T05:27:19Z 2017-05-01T05:27:19Z 2017 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Al-Tayyan, A., Assaleh, K., & Shanableh, T. (2017). Decision-level fusion for single-view gait recognition with various carrying and clothing conditions. Image and Vision Computing, 61, 54-69. doi:10.1016/j.imavis.2017.02.004 0262-8856 http://hdl.handle.net/11073/8817 10.1016/j.imavis.2017.02.004 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.relation.none.fl_str_mv | http://doi.org/10.1016/j.imavis.2017.02.004 |
| dc.subject.none.fl_str_mv | Biometrics Gait recognition Decision-level fusion Accumulated prediction image Accumulated flow image Edge-masked active energy image Multilinear subspace learning |
| dc.title.none.fl_str_mv | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| dc.type.none.fl_str_mv | Postprint Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Gait Recognition is one of the latest and attractive biometric techniques, due to its potential in identification of individuals at a distance, unobtrusively and even using low resolution images. In this paper we focus on single lateral view gait recognition with various carrying and clothing conditions. Such a system is needed in access control applications whereby a single view is imposed by the system setup. The gait data is firstly processed using three gait representation methods as the features sources; Accumulated Prediction Image (API) and two new gait representations namely; Accumulated Flow Image (AFI) and Edge-Masked Active Energy Image (EMAEI). Secondly, each of these methods is tested using three matching classification schemes; image projection with Linear Discriminant Functions (LDF), Multilinear Principal Component Analysis (MPCA) with K-Nearest Neighbor (KNN) classifier and the third method: MPCA plus Linear Discriminant Analysis (MPCA+LDA) with KNN classifier. Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. This arrangement results into nine recognition subsystems. Decisions from the nine classifiers are fused using decision-level (majority voting) scheme. A comparison between unweighted and weighted voting schemes is also presented. The methods are evaluated on CASIA B Dataset using four different experimental setups, and on OU-ISIR Dataset B using two different setups. The experimental results show that the classification accuracy of the proposed methods is encouraging and outperforms several state-of-the-art gait recognition approaches reported in the literature. |
| format | article |
| id | aus_794c56a2bf393e5daaab755dcfd5b6a3 |
| identifier_str_mv | Al-Tayyan, A., Assaleh, K., & Shanableh, T. (2017). Decision-level fusion for single-view gait recognition with various carrying and clothing conditions. Image and Vision Computing, 61, 54-69. doi:10.1016/j.imavis.2017.02.004 0262-8856 10.1016/j.imavis.2017.02.004 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8817 |
| publishDate | 2017 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Decision-level fusion for single-view gait recognition with various carrying and clothing conditionsAl-Tayyan, AmerAssaleh, KhaledShanableh, TamerBiometricsGait recognitionDecision-level fusionAccumulated prediction imageAccumulated flow imageEdge-masked active energy imageMultilinear subspace learningGait Recognition is one of the latest and attractive biometric techniques, due to its potential in identification of individuals at a distance, unobtrusively and even using low resolution images. In this paper we focus on single lateral view gait recognition with various carrying and clothing conditions. Such a system is needed in access control applications whereby a single view is imposed by the system setup. The gait data is firstly processed using three gait representation methods as the features sources; Accumulated Prediction Image (API) and two new gait representations namely; Accumulated Flow Image (AFI) and Edge-Masked Active Energy Image (EMAEI). Secondly, each of these methods is tested using three matching classification schemes; image projection with Linear Discriminant Functions (LDF), Multilinear Principal Component Analysis (MPCA) with K-Nearest Neighbor (KNN) classifier and the third method: MPCA plus Linear Discriminant Analysis (MPCA+LDA) with KNN classifier. Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. This arrangement results into nine recognition subsystems. Decisions from the nine classifiers are fused using decision-level (majority voting) scheme. A comparison between unweighted and weighted voting schemes is also presented. The methods are evaluated on CASIA B Dataset using four different experimental setups, and on OU-ISIR Dataset B using two different setups. The experimental results show that the classification accuracy of the proposed methods is encouraging and outperforms several state-of-the-art gait recognition approaches reported in the literature.Elsevier2017-05-01T05:27:19Z2017-05-01T05:27:19Z2017PostprintPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAl-Tayyan, A., Assaleh, K., & Shanableh, T. (2017). Decision-level fusion for single-view gait recognition with various carrying and clothing conditions. Image and Vision Computing, 61, 54-69. doi:10.1016/j.imavis.2017.02.0040262-8856http://hdl.handle.net/11073/881710.1016/j.imavis.2017.02.004en_UShttp://doi.org/10.1016/j.imavis.2017.02.004oai:repository.aus.edu:11073/88172024-08-22T12:08:40Z |
| spellingShingle | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions Al-Tayyan, Amer Biometrics Gait recognition Decision-level fusion Accumulated prediction image Accumulated flow image Edge-masked active energy image Multilinear subspace learning |
| status_str | publishedVersion |
| title | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| title_full | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| title_fullStr | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| title_full_unstemmed | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| title_short | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| title_sort | Decision-level fusion for single-view gait recognition with various carrying and clothing conditions |
| topic | Biometrics Gait recognition Decision-level fusion Accumulated prediction image Accumulated flow image Edge-masked active energy image Multilinear subspace learning |
| url | http://hdl.handle.net/11073/8817 |