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Video modeling and learning on Riemannian manifold for emotion recognition in the wild

机译：在黎曼流形上进行视频建模和学习以进行野外情感识别

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摘要

In this paper, we present the method for our submission to the emotion recognition in the wild challenge (EmotiW). The challenge is to automatically classify the emotions acted by human subjects in video clips under real-world environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/ distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, and partial least squares, are investigated for comparisons. Finally, an optimal fusion of classifiers learned from different kernels and different modalities (video and audio) is conducted at the decision level for further boosting the performance. We perform extensive evaluations on the EmotiW 2014 challenge data (including validation set and blind test set), and evaluate the effects of different components in our pipeline. It is observed that our method has achieved the best performance reported so far. To further evaluate the generalization ability, we also perform experiments on the EmotiW 2013 data and two well-known lab-controlled databases: CK+ and MMI. The results show that the proposed framework significantly outperforms the state-of-the-art methods.
机译：在本文中，我们提出了一种用于在野外挑战（EmotiW）中进行情感识别的方法。面临的挑战是在现实环境下，自动将人类对象在视频片段中表现出的情绪进行分类。在我们的方法中，每个视频片段都可以分别由三种类型的图像集模型（即线性子空间，协方差矩阵和高斯分布）表示，它们都可以被视为位于某些黎曼流形上的点。然后，在这些集合模型上分别采用不同的黎曼核，以进行相似度/距离测量。对于分类，研究了三种类型的分类器，即内核支持向量机，逻辑回归和偏最小二乘以进行比较。最后，在决策层进行从不同内核和不同模式（视频和音频）中学习的分类器的最佳融合，以进一步提高性能。我们对EmotiW 2014挑战数据（包括验证集和盲测集）进行了广泛的评估，并评估了产品线中不同组件的影响。可以观察到，我们的方法迄今已达到最佳性能。为了进一步评估泛化能力，我们还对EmotiW 2013数据和两个著名的实验室控制数据库CK +和MMI进行了实验。结果表明，所提出的框架明显优于最新方法。

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  • 来源
    《Journal on multimodal user interfaces》 |2016年第2期|113-124|共12页
  • 作者

    Liu Mengyi; Wang Ruiping; Li Shaoxin; Huang Zhiwu; Shan Shiguang; Chen Xilin;

  • 作者单位

    Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China;

    Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China;

    Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China;

    Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China;

    Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China;

    Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Emotion recognition; Video modeling; Riemannian manifold; EmotiW challenge;

    机译：情绪识别;视频建模;黎曼流形;情绪挑战;

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