基于数据融合的表情识别

本文为了解决表情识别中单一数据所包含人脸表情信息不全面的问题，融合了图像与标记点数据；针对传统模式识别方法中手动提取特征的复杂性，采用了神经网络框架，从而实现特征的自动提取。本文算法以人脸表情的图像与标记点数据为基础，以神经网络为框架，采用稀疏自动编码器对网络进行预训练，实现了网络的稀疏连接，另外，在网络权值更新过程中结合了结构化正则项（Structured Regularization），限制了不同数据与隐层神经元的连接。实验表明：图像与标记点数据的融合更全面地表达了人脸表情信息；稀疏自动编码器和结构化正则项的运用能更有效的提取关键特征并使神经网络自动分析不同输入数据在表情识别中所起到的作用强弱。

Facial expression recognition based on data fusion

To solve the incompleteness of single data for facial expression recognition, we concatenated images and mark points as inputs. For the complexity of hand-crafted feature in convolutional pattern recognition methods, we adopted neural network to extract feature automatically. The method concatenated images and mark points as basis and framed by neural network. Sparse Auto-encoder was used to pre-train the network and make the network sparse. In addition, structured regularization was added to restrict the connection between different inputs and neuron in hidden layer. The experimental results showed that the concatenation of images and mark points could present the facial expression more thoroughly. The application of Sparse Auto-encoder and structured regularization could help the network extract the key feature effectively and learn the importance of different data to facial expression recognition automatically.