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Laplacian smoothing transform for face recognition

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Abstract

In this paper, we investigate how to extract the lowest frequency features from an image. A novel Laplacian smoothing transform (LST) is proposed to transform an image into a sequence, by which low frequency features of an image can be easily extracted for a discriminant learning method for face recognition. Generally, the LST is able to be an efficient dimensionality reduction method for face recognition problems. Extensive experimental results show that the LST method performs better than other pre-processing methods, such as discrete cosine transform (DCT), principal component analysis (PCA) and discrete wavelet transform (DWT), on ORL, Yale and PIE face databases. Under the leave one out strategy, the best performance on the ORL and Yale face databases is 99.75% and 99.4%; however, in this paper, we improve both to 100% with a fast linear feature extraction method for the first time.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE); Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    SuiCheng Gu, Ying Tan & XinGui He

Authors
  1. SuiCheng Gu
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  2. Ying Tan
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  3. XinGui He
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Correspondence to Ying Tan.

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Gu, S., Tan, Y. & He, X. Laplacian smoothing transform for face recognition. Sci. China Inf. Sci. 53, 2415–2428 (2010). https://doi.org/10.1007/s11432-010-4099-1

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  • DOI: https://doi.org/10.1007/s11432-010-4099-1

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