基于深度通用特征和支持向量机的面部表情识别

2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS) Pub Date : 2016-09-01 DOI:10.1109/NICS.2016.7725672

Duc Minh Vo, T. Le

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引用次数: 19

摘要

基于模式识别领域的最新趋势——卷积神经网络(CNN)，提出了一种基于卷积神经网络和支持向量机(SVM)的人脸表情识别新方法。我们的研究将CNN和SVM的深度通用特征结合在一起，比单独使用CNN更有效。在Cohn-Kanade数据集上对该方法进行了验证，准确率达到96.04%，优于其他方法。

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Deep generic features and SVM for facial expression recognition

Motivated by the newly recent trend in pattern recognition - convolutional neural network (CNN), we introduce a new fusion method based on CNN and support vector machines (SVM) for facial expression recognition problem. Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only. We investigate our proposed method on Cohn-Kanade dataset and achieve 96.04% in accuracy rate which is better than other state-of-the-art methods.

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来源期刊

2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)

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