基于改进卷积神经网络与支持向量机结合的面部表情识别算法

针对当前卷积神经网络（CNN）利用端层特征进行面部表情识别存在模型结构繁琐、训练参数过多、识别不够理想的问题，提出一种基于改进CNN与支持向量机（SVM）相结合的优化算法。首先，利用连续卷积的思想设计网络模型，以获取更多非线性激活；然后，采用自适应全局平均池化（GAP）层取代传统CNN中的全连接层，以减少网络参数量；最后，用SVM分类器代替传统Softmax函数实现表情识别，以提高模型泛化能力。实验结果表明，所提算法在Fer2013和CK+数据集上分别取得了73.4%和98.06%的识别准确率，与传统LeNet-5算法相比，在Fer2013数据集上提升了2.2个百分点，且该网络模型结构简单、参数量较少，具有良好的鲁棒性。

Facial expression recognition algorithm based on combination of improved convolutional neural network and support vector machine

In view of the problems of the current Convolutional Neural Network （CNN） using end layer features to recognize facial expression， such as complex model structure， too many parameters and unsatisfactory recognition， an optimization algorithm based on the combination of improved CNN and Support Vector Machine （SVM） was proposed. First， the network model was designed by the idea of continuous convolution to obtain more nonlinear activations. Then， the adaptive Global Average Pooling （GAP） layer was used to replace the fully connected layer in traditional CNN to reduce the network parameters. Finally， in order to improve generalization ability of the model， SVM classifier instead of the traditional Softmax function was used to realize expression recognition. Experimental results show that the proposed algorithm achieves 73.4% and 98.06% recognition accuracy on Fer2013 and CK+ datasets， which is 2.2 percentage points higher than the traditional LeNet-5 algorithm on Fer2013 dataset. Moreover， this network model has simple structure， less parameters and good robustness.