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Exploiting privileged information for facial expression recognition

机译：利用面部表情识别的特权信息

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Most of the facial expression recognition methods consider that both training and testing data are equally distributed. As facial image sequences may contain information for heterogeneous sources, facial data may be asymmetrically distributed between training and testing, as it may be difficult to maintain the same quality and quantity of information. In this work, we present a novel classification method based on the learning using privileged information (LUPI) paradigm to address the problem of facial expression recognition. We introduce a probabilistic classification approach based on conditional random fields (CRFs) to indirectly propagate knowledge from privileged to regular feature space. Each feature space owns specific parameter settings, which are combined together through a Gaussian prior, to train the proposed t-CRF+ model and allow the different tasks to share parameters and improve classification performance. The proposed method is validated on two challenging and publicly available benchmarks on facial expression recognition and improved the state-of-the-art methods in the LUPI framework.
机译：大多数面部表情识别方法认为，训练和测试数据都同样分布。由于面部图像序列可以包含异构源的信息，因此面部数据可以在训练和测试之间不对称地分布，因为可能难以保持相同的质量和信息量。在这项工作中，我们介绍了一种基于使用特权信息（LUPI）范例的学习的新型分类方法来解决面部表情识别问题。我们将基于条件随机字段（CRF）的概率分类方法进行间接传播从特权传播到常规特征空间的知识。每个特征空间都拥有特定的参数设置，这些参数设置通过高斯先前组合在一起，培训所提出的T-CRF +模型，并允许不同的任务共享参数并提高分类性能。拟议的方法是关于面部表情识别的两个具有挑战性和公开的基准，并改善了LUPI框架中的最先进方法。

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