基于图像序列的学习表情识别

Learning Expression Recognition Based On Image Sequence

对学习者面部表情进行识别,能够判断学习者的情绪状态,分析其学习效果。针对面部表情具有持续性和时序性的特点,采用表情图像序列作为表情识别对象。通过组合网络的方式,将长短期记忆网络(Long Short Term Memory Network, LSTM)与VGGNet组合成VGGNet-LSTM模型,在此基础上进行表情识别,显著提高了识别准确率。借鉴迁移学习方法,将VGGNet通过基本表情数据集CK+进行预训练后迁移到学习表情数据集下,避免了学习表情数据集数据量不足的缺陷,解决了模型过拟合问题。

By recognizing the facial expressions,the emotional state of learner can be judged and their learning effect can be analyzed.Due to the persistence and timing of facial expressions,the sequence of facial images is adopted as the object of facial expression recognition.The Long Short Term Memory Network(LSTM)and VGGNet are combined into a VGGNET-LSTM model.On this basis,facial expression recognition is carried out,which significantly improves the accuracy of recognition.Based on the transfer learning method,VGGNet is transferred to the learning expression data set after being pre-trained through the basic expression data set CK+and avoids the defect of insufficient data in the learning expression data set and solves the problem of overfitting the model.