一种识别表情序列的卷积神经网络

传统的人脸表情识别方法需要人为指定特征训练方向，卷积神经网络方法虽然可以自动训练分类特征，但是存在无法识别表情序列的弊端．针对此问题，运用一种多网络融合技术，使构建的网络能够对表情序列进行识别．网络构建方法为:首先构建多个卷积神经网络，使每个网络处理一帧图片；然后将处理结果在融合层进行融合；最后通过一个分类器输出识别结果．在CK+人脸表情数据库上，分别对3帧、4帧和5帧表情序列进行实验，均获得了较高的识别率．

Multiple convolutional neural networks for facial expression sequence recognition

As an important part of the human-computer interaction system, facial expression recognition has been a hot research field. The convolutional neural network cannot recognize expression sequence although it can train the classification features automatically for the reason that the direction of feature training need to be specified manually. In order to solve this problem, this paper improves the network structure, and proposes a multi convolutional network fusion method that can be used to identify the expression sequences containing multiple frames. First, we construct a number of convolutional neural networks, so that each network processes one frame, and then merge the results in the merge layer, and finally pass the softmax classifier to give the identity result. On the CK+facial expression database, experiments are carried out on the 3rd, 4th and 5th frames of expression sequences, and a high recognition rate is obtained for all experiments.