Shape primitive histogram: low‐level face representation for face recognition
Sheng Huang, Dan Yang, Haopeng Zhang, Luwen Huangfu, Xiaohong Zhang · IET Biometrics · 2014
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.