融合边缘检测和递归神经网络的视频表情识别

为有效解决传统视频人脸表情识别通常只关注单张视频帧的空间特征,而忽略了相邻帧之间隐藏的时间特征的问题,提出一种结合边缘检测和递归神经网络的视频表情识别方法,利用梯度边缘检测准确地提取输入图像的纹理信息,同时提出一种分片交叉LSTM结构,提取出图像序列中隐藏的时空特征。实验在CK+和MMI视频库上进行,在OCNN-RNN网络中分别取得88.4%和69.7%的识别率,在GCNN-RNN网络中分别取得89.8%和73.6%的识别率,最终使用提出的加权随机搜索方法融合GCNN-RNN和OCNN-RNN两个网络之后,分别取得了94.6%和79.9%的识别率,均优于单流网络算法,证明了所提算法的有效性。

Video facial emotion recognition based on edge detection and recurrent neural network

In view of the existing algorithms, traditional video emotion-based facial expression recognition method only pays attention tothe spatial features of a single video frame, and ignores the hidden temporal features between adjacent frames. Therefore, this paperproposes a novel method to extract features using edge detection and improved recurrent neural network. Gradient edge detection canextract texture information of video frame in a more accurate way,at the same time, a kind of overlapping LSTM structure is proposed,and the recurrent neural network can acquire the hidden spatio-temporal information from the input frames. The experiments in this paperare carried out on the CK + and MMI video database. the result of 88. 4% and 69. 7% are obtained in the OCNN-RNN networkrespectively, and the outcome of 89. 8% and 73. 6% are acquired in the GCNN-RNN network from each database. and finally the randomsearch is used to weight the fusion of the results of the GCNN-RNN network and the OCNN-RNN network. After the two networks arefinally merged, the average recognition rate of the integrated model is 94. 6% and 79. 9% respectively, and the accuracy is better thanother algorithms, the effectiveness of the proposed algorithm is proved.