3D Facial Expression Recognition Using Deep Feature Fusion CNN

As an important way of human communication, facial expression not only reflects our mental activities but also provides useful information for human behavior research. Recently, 3D technology becomes promising method to achieve robust facial expression analysis. 3D face scans are robust to lighting and pose variations. In this paper, a novel deep feature fusion convolution neural network (CNN) is designed for 3D facial expression recognition (FER). Each 3D face scan is firstly represented as 2D facial attribute maps (including depth, normal, and shape index values). Then, we combine different of facial attribute maps to learn facial representations by fine-tuning a pre-trained deep feature fusion CNN subnet trained from a large-scale image dataset for universal visual tasks. Moreover, Global Average Pooling is utilized to reduce the number of parameters to decrease the effect of overfitting. The experiments are conducted on the Bosphorus database and the results demonstrate the effectiveness of the proposed method.