Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition

Sheng Huang, Dan Yang, Haopeng Zhang, Luwen Huangfu, Xiaohong Zhang

We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition. Since human faces exist abundant shape features, we address the face representation issue from the perspective of the shape feature extraction. In our approach, we divide faces into a number of tiny shape fragments and reduce these shape fragments to several uniform atomic shape patterns called Shape Primitives. A convolution with Haar Wavelet templates is applied to each shape fragment to identify its belonging shape primitive. After that, we do a histogram statistic of shape primitives in each spatial local image patch for incorporating the spatial information. Finally, each face is represented as a feature vector via concatenating all the local histograms of shape primitives. Four popular face databases, namely ORL, AR, Yale-B and LFW-a databases, are employed to evaluate SPH and experimentally study the choices of the parameters. Extensive experimental results demonstrate that the proposed approach outperform the state-of-the-arts.

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