Face recognition based on separable lattice 2-D HMM with state duration modeling
Face recognition based on separable lattice 2-D HMM with state duration modeling
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DOI：
10.1109/icassp.2010.5495625
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Y. Takahashi;Akira Tamamori;Yoshihiko Nankaku;K. Tokuda
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文献类型：
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作者：
Y. Takahashi;Akira Tamamori;Yoshihiko Nankaku;K. Tokuda
本文描述了使用状态持续时间模型进行图像识别的可分离晶格二维 HMM (SL-HMM) 的扩展。 SL-HMM 是基于 HMM 状态转换而具有大小和位置不变性的生成模型。然而，HMM 的状态持续时间概率随着持续时间的增加呈指数下降，因此它可能不适合精确地建模图像变化。为了克服这个问题，我们采用隐半马尔可夫模型（HSMM）的结构，其中状态持续时间概率由参数分布显式建模。人脸识别实验表明，所提出的模型提高了具有大小和位置变化的图像的性能。
This paper describes an extension of separable lattice 2-D HMMs (SL-HMMs) using state duration models for image recognition. SL-HMMs are generative models which have size and location invariances based on state transition of HMMs. However, the state duration probability of HMMs exponentially decreases with increasing duration, therefore it may not be appropriate for modeling image variations accuratelty. To overcome this problem, we employ the structure of hidden semi Markov models (HSMMs) in which the state duration probability is explicitly modeled by parametric distributions. Face recognition experiments show that the proposed model improved the performance for images with size and location variations.