基于多特征融合密集残差CNN的人脸表情识别

FACIAL EXPRESSION RECOGNITION BASED ON MULTI-FEATURE FUSION DENSE RESIDUAL CNN

传统人脸表情识别主要基于人工提取特征,其存在算法鲁棒性较差、易受人脸身份信息干扰等问题,以及传统卷积神经网络不能充分提取人脸表情特征的现状。对此提出一种基于多特征融合密集残差卷积神经网络的人脸表情识别。该方法能够充分利用神经网络中每层的特征,在密集块中,对于每一个卷积层,其前面所有卷积层的输出都将作为本卷积层的输入。然后将每个密集块的输出送入到全连接层中进行特征融合,经过Softmax分类器分类。在CK+和FER2013数据集上进行多次实验,与传统的机器学习方法相比,该方法具有较高的准确率与较强的鲁棒性。

Because the traditional facial expression recognition is mainly based on the artificial extraction of features,the robustness of the algorithm is poor,and it is easy to be interfered by the face identity information. Traditional CNN cannot adequately extract facial expression features. We proposed a facial expression recognition based on multi-feature fusion dense residual CNN. This method could make full use of the characteristics of each layer in the neural network. For each convolution layer in the dense block,the output of all convolution layers in front of it would be the input of this convolution layer. Then the output of each dense block was fed into the full connection layer for feature fusion,and classified by Softmax classifier. Experiments were performed on CK+ and FER2013 data sets. Compared with traditional machine learning,our method has higher accuracy and stronger robustness.