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Facial expression recognition through adaptive learning of local motion descriptor

机译：通过自适应学习局部运动描述符实现表情识别

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摘要

A novel bag-of-words based approach is proposed for recognizing facial expressions corresponding to each of the six basic prototypic emotions from a video sequence. Each video sequence is represented as a specific combination of local (in spatio-temporal scale) motion patterns. These local motion patterns are captured in motion descriptors (MDs) which are unique combinations of optical flow and image gradient. These MDs can be compared to the words in the bag-of-words setting. Generally, the key-words in the wordbook as reported in the literature, are rigid, i.e., are taken as it is from the training data and cannot generalize well. We propose a novel adaptive learning technique for the key-words. The adapted key-MDs better represent the local motion patterns of the videos and generalize well to the unseen data and thus give better expression recognition accuracy. To test the efficiency of the proposed approach, we have experimented extensively on three well known datasets. We have also compared the results with existing state-of-the-art expression descriptors. Our method gives better accuracy. The proposed approach have been able to reduce the training time including the time for feature-extraction more than nine times and test time more than twice as compared to current state-of-the-art descriptor.
机译：提出了一种新颖的基于词袋的方法，用于从视频序列中识别与六个基本原型情感中的每一个相对应的面部表情。每个视频序列表示为局部（时空尺度）运动模式的特定组合。这些局部运动模式被捕获在运动描述符（MD）中，该描述符是光流和图像梯度的唯一组合。可以将这些MD与“词袋”设置中的词进行比较。通常，文献中报道的单词书中的关键词是僵化的，即，它们是从训练数据中直接获取的，不能很好地概括。我们提出了一种新颖的关键词自适应学习技术。改编的关键MD更好地表示了视频的局部运动模式，并很好地泛化了看不见的数据，因此提供了更好的表情识别精度。为了测试所提出方法的效率，我们在三个众所周知的数据集上进行了广泛的实验。我们还将结果与现有的最新表达描述符进行了比较。我们的方法具有更好的准确性。与当前的最新描述符相比，所提出的方法已经能够减少训练时间，其中包括特征提取时间超过9倍，测试时间超过2倍。

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