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Sparse Representation Shape Models

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An Erratum to this article was published on 14 December 2013

Abstract

It is well-known that, during shape extraction, enrolling an appropriate shape constraint model could effectively improve locating accuracy. In this paper, a novel deformable shape model, Sparse Representation Shape Models (SRSM), is introduced. Rather than following commonly utilized statistical shape constraints, our model constrains shape appearance based on a morphological structure, the convex hull of aligned training samples, i.e., only shapes that could be linearly represented by aligned training samples with the sum of coefficients equal to one, are defined as qualified. This restriction strictly controls shape deformation modes to reduce extraction errors and prevent extremely poor outputs. This model is realized based on sparse representation, which ensures during shape regularization the maximum valuable shape information could be reserved. Besides, SRSM is interpretable and hence helpful to further understanding applications, such as face pose recognition. The effectiveness of SRSM is verified on two publicly available face image datasets, the FGNET and the FERET.

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Notes

  1. Before processing, each input shape must be aligned with the mean of training samples to unify coordinate system.

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Author information

Authors and Affiliations

  1. School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin, P.R. China

    Yuelong Li & Jigang Wu

  2. Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R. China

    Jufu Feng

  3. Automobile Transport Command Department, Military Transportation University, Tianjin, P.R. China

    Li Meng

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  1. Yuelong Li
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  2. Jufu Feng
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  3. Li Meng
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  4. Jigang Wu
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Correspondence to Yuelong Li.

Additional information

This work was partially supported by the National Basic Research Program of China (2011CB302400) and the National Natural Science Foundation of China (61173032).

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Li, Y., Feng, J., Meng, L. et al. Sparse Representation Shape Models. J Math Imaging Vis 48, 83–91 (2014). https://doi.org/10.1007/s10851-012-0394-3

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