Shape primitive histogram: low-level face representation for face recognition

Human face contains abundant shape features. This fact motivates a lot of shape feature-based face detection and three-dimensional (3D) face recognition approaches. However, as far as we know, there is no prior low-level face representation which is purely based on shape feature proposed for conventional 2D (image-based) face recognition. In this study, the authors present a novel low-level shape-based face representation named `shape primitives histogram´ (SPH) for face recognition. In this approach, the face images are separated into a number of tiny shape fragments and they reduce these shape fragments to several uniform atomic shape patterns called `shape primitives´. Then the face representation is obtained by implementing a histogram statistic of shape primitives in a local image region. To take scale information into consideration, they also produce multi-scale SPHs (MSPHs) by concatenating the SPHs extracted from different scales. Moreover, they experimentally study the influences of each stage of SPH computation on performance, concluding that a small cell with 1/2 overlap and a fine size block with 1/2 overlap are important for good results. Four popular face databases, namely ORL, AR, YaleB and LFW-a, are employed to evaluate SPH and MSPH. Surprisingly, such seemingly naive shape-based face representations outperform the state-of-the-art low-level face representations.