Face recognition based on separable lattice 2-D HMMS using variational bayesian method
Face recognition based on separable lattice 2-D HMMS using variational bayesian method
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DOI：
10.1109/icassp.2012.6288351
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Kei Sawada;Akira Tamamori;Kei Hashimoto;Yoshihiko Nankaku;K. Tokuda
中科院分区：
文献类型：
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作者：
Kei Sawada;Akira Tamamori;Kei Hashimoto;Yoshihiko Nankaku;K. Tokuda
本文提出了一种基于变分贝叶斯方法的可分离晶格二维hmm （sl2d - hmm）图像识别技术。sl2d - hmm的提出是为了减少几何变化的影响，例如尺寸和位置。最大似然标准以前曾用于训练sl2d - hmm。然而，在许多图像识别任务中，很难使用足够的训练数据，并且存在过拟合问题。利用贝叶斯准则可以提高基于模型边缘化的泛化能力，并利用模型参数的有用先验信息作为先验分布。人脸识别实验表明，该方法提高了图像识别效果。
This paper proposes an image recognition technique based on separable lattice 2-D HMMs (SL2D-HMMs) using the variational Bayesian method. SL2D-HMMs have been proposed to reduce the effect of geometric variations, e.g., size and location. The maximum likelihood criterion had previously been used in training SL2D-HMMs. However, in many image recognition tasks, it is difficult to use sufficient training data, and it suffers from the over-fitting problem. A higher generalization ability based on model marginalization is expected by applying the Bayesian criterion and useful prior information on model parameters can be utilized as prior distributions. Experiments on face recognition indicated that the proposed method improved image recognition.