What question did this study set out to answer?

To develop a scalable incremental principal component analysis algorithm with mathematically bounded approximation error for face-recognition systems.

July 21, 2006IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)

A novel incremental principal component analysis and its application for face recognition

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Authors

HZHaitao ZhaoNational University of Defense Technology PYPong C. YuenHong Kong Baptist University JKJames T. KwokHong Kong University of Science and Technology

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Implication

Experimental evaluation demonstrates near-batch recognition accuracy in standard benchmark face databases, indicating scalable incremental principal component analysis with bounded error.

Key Points

To develop a scalable incremental principal component analysis algorithm with mathematically bounded approximation error for face-recognition systems.
Formulated a singular value decomposition updating-based incremental principal component analysis (SVDU-IPCA) algorithm featuring theoretical error bounds and kernel extension capabilities.
Evaluated recognition performance against standard batch-mode principal component analysis using public benchmark databases (FERET, AR, and Yale B).
Mathematically established that the approximation error of the SVDU-IPCA algorithm remains strictly bounded during incremental updates.
Demonstrated that the difference in average face-recognition accuracy between the proposed incremental method and standard batch-mode principal component analysis was less than 1% across the evaluated databases.

Cite This Study

Zhao et al. (2006) studied this question.

synapsesocial.com/papers/6a19be1d5085da102eea645b https://doi.org/10.1109/tsmcb.2006.870645

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Implication

Experimental evaluation demonstrates near-batch recognition accuracy in standard benchmark face databases, indicating scalable incremental principal component analysis with bounded error.

A novel incremental principal component analysis and its application for face recognition

Discussion

Member takes

Implication

Key Points

Cite This Study

A novel incremental principal component analysis and its application for face recognition

Discussion

Member takes

Implication

Key Points

Cite This Study

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