Facial Expression Recognition Based on Multiscale Features and Attention Mechanism

Facial features extracted from deep convolutional networks are susceptible to background, individual identity and other factors. It interferes with facial expression recognition when mixed with useless features. Considering that different scale features have rich semantic and texture information respectively, this paper takes VGG-16 as the basic network structure and combines multiscale features to obtain richer feature information. In addition, the input feature map elements are enhanced or suppressed by the attention module in order to extract salient features more accurately. The proposed method was validated on two commonly used expression data sets CK+ and RAF-DB, and the recognition rates were 98.77 and 82.83%, respectively. Experimental results show the superiority of this method.