Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
This work addresses face recognition for computer vision applications, but it is incremental as it builds on existing Haar wavelet techniques.
The authors tackled face recognition by introducing a novel low-level representation called Shape Primitives Histogram (SPH), which extracts shape features from faces using Haar wavelets and spatial histograms, and demonstrated that it outperforms state-of-the-art methods on four popular face databases.
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.