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Liu, Xiaoming; Chen, Tsuhan; Thornton, Susan M.

Eigenspace updating for non-stationary process and its application to face recognition. (English) Zbl 1035.68104

Pattern Recognition 36, No. 9, 1945-1959 (2003).
Summary: We introduce a novel approach to modeling non-stationary random processes. Given a set of training samples sequentially, we can iteratively update the eigenspace to manifest the current statistics provided by each new sample. The updated eigenspace is derived based more on recent samples and less on older samples, controlled by a number of decay parameters. Extensive study has been performed on how to choose these decay parameters. Other existing eigenspace updating algorithms can be regarded as special cases of our algorithm. We show the effectiveness of the proposed algorithm with both synthetic data and practical applications on face recognition. Significant improvements have been observed on face images with different variations, such as pose, expression and illumination variations. We expect the proposed algorithm to have other applications in active recognition and modeling as well.

Cited in 1 Document

MSC:

68T10 Pattern recognition, speech recognition

Keywords:

Principal component analysis; Eigenspace updating; Non-stationary process; Face recognition

Software:

CMU PIE
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Full Text: DOI

References:

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