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Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
| Content Provider | CiteSeerX |
|---|---|
| Author | Huanga, Sheng Yanga, Dan Zhangc, Haopeng Huangfud, Luwen Zhangb, Xiaohong |
| Abstract | Human face contains abundant shape features. This fact motivates a lot of impressive shape feature-based face detec-tion and 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 paper, we present a novel low-level shape-based face representation named Shape Primitives Histogram (SPH) for face recogni-tion. In this approach, the face images are separated into a number of tiny shape fragments and we 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. In order to take scale information into consideration, we also produce Multi-scale Shape Primitive Histograms (MSPH) by concatenating the SPHs ex-tracted from different scales. Moreover, we 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 databases, 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. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
