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首页> 外文期刊>Image and Vision Computing >Pose-specific non-linear mapings in feature space towards multiview facial expression recognition
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Pose-specific non-linear mapings in feature space towards multiview facial expression recognition

机译：特征空间中面向多视图面部表情识别的特定于姿势的非线性映射

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We introduce a novel approach to recognizing facial expressions over a large range of head poses. Like previous approaches, we map the features extracted from the input image to the corresponding features of the face with the same facial expression but seen in a frontal view. This allows us to collect all training data into a common referential and therefore benefit from more data to learn to recognize the expressions. However, by contrast with such previous work, our mapping depends on the pose of the input image: We first estimate the pose of the head in the input image, and then apply the mapping specifically learned for this pose. The features after mapping are therefore much more reliable for recognition purposes. In addition, we introduce a non-linear form for the mapping of the features, and we show that it is robust to occasional mistakes made by the pose estimation stage. We evaluate our approach with extensive experiments on two protocols of the BU3DFE and Multi-PIE datasets, and show that it outperforms the state-of-the-art on both datasets. (C) 2016 Elsevier B.V. All rights reserved.
机译：我们介绍一种新颖的方法来识别各种头部姿势下的面部表情。像以前的方法一样，我们将从输入图像中提取的特征映射到具有相同面部表情但在正面视图中看到的面部的相应特征。这使我们能够将所有训练数据收集到一个共同的参考中，因此可以从更多的数据中受益，以学习识别这些表达。但是，与之前的工作相比，我们的映射取决于输入图像的姿势：我们首先估计输入图像中头​​部的姿势，然后应用针对该姿势专门学习的映射。因此，映射后的特征出于识别目的更加可靠。此外，我们为特征映射引入了一种非线性形式，并且证明了它对于姿态估计阶段偶尔出现的错误具有鲁棒性。我们在BU3DFE和Multi-PIE数据集的两个协议上进行了广泛的实验，对我们的方法进行了评估，结果表明，该方法在两个数据集上都优于最新技术。 （C）2016 Elsevier B.V.保留所有权利。

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