A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis
This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usu...
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| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2018-05, Vol.48 (5), p.1200-1217 |
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| container_title | Applied intelligence (Dordrecht, Netherlands) |
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| creator | Portillo-Portillo, Jose Leyva, Roberto Sanchez, Victor Sanchez-Perez, Gabriel Perez-Meana, Hector Olivares-Mercado, Jesus Toscano-Medina, Karina Nakano-Miyatake, Mariko |
| description | This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy. |
| doi_str_mv | 10.1007/s10489-017-1043-8 |
| format | Article |
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Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy.</description><identifier>ISSN: 0924-669X</identifier><identifier>EISSN: 1573-7497</identifier><identifier>DOI: 10.1007/s10489-017-1043-8</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Artificial Intelligence ; Computer Science ; Discriminant analysis ; Energy consumption ; Gait recognition ; Invariants ; Machines ; Manufacturing ; Mechanical Engineering ; Processes</subject><ispartof>Applied intelligence (Dordrecht, Netherlands), 2018-05, Vol.48 (5), p.1200-1217</ispartof><rights>Springer Science+Business Media, LLC 2017</rights><rights>Applied Intelligence is a copyright of Springer, (2017). 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Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy.</description><subject>Accuracy</subject><subject>Artificial Intelligence</subject><subject>Computer Science</subject><subject>Discriminant analysis</subject><subject>Energy consumption</subject><subject>Gait recognition</subject><subject>Invariants</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mechanical 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| subjects | Accuracy Artificial Intelligence Computer Science Discriminant analysis Energy consumption Gait recognition Invariants Machines Manufacturing Mechanical Engineering Processes |
| title | A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis |
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