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Información general |
| Autor |
Meng, Jicheng; Yang, Yuming |
| Publicado |
InTech Open Access Publisher, 2012
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| Abstract |
In this paper, weextensively investigate
symmetrical two‐dimensional principal component
analysis (S2DPCA) and introduce two image measures
for S2DPCA‐based face recognition, volume measure
(VM) and subspace distance measure (SM). Although
symmetrical featuresare an obviously but not absolutely
facial characteristic, they have been successfully applied
to PCA and 2DPCA. The paper gives detailed evidence
that even and odd subspaces in S2DPCA are mutually
orthogonal, and particularly that S2DPCA can be
constructed using a quarter of the conventional S2DPCA
even/odd covariance matrix. Based on these theories, we
investigate the time and memory complexities of
S2PDCA further, and find that S2DPCA can in fact be
computed using a quarter of the time and memory
compared to conventional S2DPCA. Finally, VM and SM
are introduced to S2DPCA for final classification. Our
experiments compare S2DPCA with 2DPCA on YALE,
AR and FERET face databases, and the results indicate
that S2DPCA+VM generally outperforms other
algorithms. |
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International Journal of Advanced Robotic Systems
Autor: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publicado: 2004
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