  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.
