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首页> 外文期刊>Journal of visual communication & image representation >Fusing dynamic deep learned features and handcrafted features for facial expression recognition
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Fusing dynamic deep learned features and handcrafted features for facial expression recognition

机译：融合动态的深度学习特征和手工特征以进行面部表情识别

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摘要

The automated recognition of facial expressions has been actively researched due to its wide-ranging applications. The recent advances in deep learning have improved the performance facial expression recognition (FER) methods. In this paper, we propose a framework that combines discriminative features learned using convolutional neural networks and handcrafted features that include shape- and appearance-based features to further improve the robustness and accuracy of FER. In addition, texture information is extracted from facial patches to enhance the discriminative power of the extracted textures. By encoding shape, appearance, and deep dynamic information, the proposed framework provides high performance and outperforms state-of-the-art FER methods on the CK+ dataset. (C) 2019 Elsevier Inc. All rights reserved.
机译：面部表情的自动识别由于其广泛的应用而受到积极研究。深度学习的最新进展已改善了面部表情识别（FER）方法的性能。在本文中，我们提出了一个框架，该框架将使用卷积神经网络学习的判别特征与包括基于形状和外观的特征在内的手工特征相结合，以进一步提高FER的鲁棒性和准确性。另外，从面部斑块提取纹理信息以增强提取的纹理的辨别力。通过对形状，外观和深层动态信息进行编码，所提出的框架在CK +数据集上提供了高性能，并且胜过了最新的FER方法。 （C）2019 Elsevier Inc.保留所有权利。

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