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首页> 外文会议>2016 2nd International Conference of Signal Processing and Intelligent Systems >Facial expression recognition with discriminatory graphical models
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Facial expression recognition with discriminatory graphical models

机译：带有识别性图形模型的面部表情识别

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摘要

Discriminating probabilistic graphical models are reliable tools for a sequence labeling task. Conditional Random Fields (CRFs) are discriminative models which will enable us to label a sequence of input data. Other variations of CRFs have been proposed. Hidden Conditional Random Fields (HCRFs) incorporate hidden states to the CRF model and assign a label for the whole input sequence as the model's output. Latent-Dynamic Conditional Random Fields (LDCRFs) also incorporate hidden variable states to the CRFs, in addition, these models are able to label each output variables separately. These models can capture subtle changes among different classes which will help us to achieve better recognition results. In this work we experiment various models and settings in order to achieve better results in facial expression recognition from sequence of videos. We use CRF and LDCRF models and train them with Limited-memory BFGS and Conjugate Gradient parameter learning methods. For each model we use various feature vectors in order to achieve better recognition results. We use Active Appearance Model (AAM) landmark points, Histogram of Oriented Gradients (HOG) and Uniform Local Binary Pattern (U-LBP) as our feature vectors in our models. We show which combination of learning methods and feature vectors are suitable for CRF and LDCRF discriminative models.
机译：区分概率图形模型是用于序列标记任务的可靠工具。条件随机场（CRF）是判别模型，它将使我们能够标记输入数据序列。已经提出了CRF的其他变型。隐藏条件随机字段（HCRF）将隐藏状态合并到CRF模型中，并为整个输入序列分配标签作为模型的输出。潜在动态条件随机字段（LDCRF）也将隐藏变量状态合并到CRF中，此外，这些模型能够分别标记每个输出变量。这些模型可以捕获不同类别之间的细微变化，这将有助于我们获得更好的识别结果。在这项工作中，我们尝试各种模型和设置，以便从视频序列中获得更好的面部表情识别结果。我们使用CRF和LDCRF模型，并使用有限内存BFGS和共轭梯度参数学习方法对其进行训练。对于每个模型，我们使用各种特征向量以实现更好的识别结果。我们使用活动外观模型（AAM）界标点，定向梯度直方图（HOG）和均匀局部二值模式（U-LBP）作为模型中的特征向量。我们展示了哪种学习方法和特征向量的组合适用于CRF和LDCRF判别模型。

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