基于唤醒效价情绪模型和深度学习方法的面部表情识别

Yong Yang, Yue Sun
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引用次数: 3

摘要

传统的面部情绪识别方法是对基本情绪进行分类。但是,基本情感理论仅限于表达微妙的、不同的情感。因此,本文采用了唤醒价连续情感空间模型,丰富了情感表达。唤起反映情绪强度,效价反映积极情绪和消极情绪。唤起和效价的值都在相同的范围内,在-1到1之间。在实验中,在预训练模型中使用卷积神经网络(CNN)和支持向量回归(SVR)。在该模型中,CNN作为训练后的特征提取器,采用SVR对唤醒值和价值进行训练和预测。通过预测值对面部表情进行预测。对比实验结果表明,该方法比传统方法具有更好的识别效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial Expression Recognition Based on Arousal- Valence Emotion Model and Deep Learning Method
The traditional facial emotion recognition method is classifying basic emotions. But, basic emotions theory is limited to express subtle and disparate emotion. So this paper uses the arousal-valence continuous emotion space model, which can enrich emotion expression. The arousal reflects emotional intensity, and the valence indicates positive and negative emotion. The arousal and valence all have the value in the same range, which is between -1 and 1. In the experiments, it uses convolutional neural network (CNN) in the pre-trained models and support vector regression(SVR). In this model, CNN works as a trained feature extractor and SVR is adopted to train and predict the values of the arousal and valence. Through the predicted values it can be predicted the facial emotion. The contrast experimental results show that the proposed method can get better recognition result than the traditional methods.
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