Multi-view Deep Neural Network Enhanced Forests for Facial Expression Recognition

In order to improve the accuracy of multi-view facial expression recognition in natural environment,a novel multi-view deep neural network enhanced random forest(M-DNF)is proposed for robust facial recognition.First,our method extracts robust deep transfer expression features from random facial patches to reduce the influence from various noises,such as occlusion,etc.Then,in order to eliminate the influence of pose various,the M-DNF is devised to enhance decision trees with the capability of representation learning from transferred convolutional neural networks and to model facial expression of different views with conditional probabilistic learning.M-DNF can achieve both head poses and facial expressions,and performs well even when there are only a small amount of training data.Experiments were conducted using public CK+,multi-view BU-3DFE and LFW datasets.Compared to the state-of-the-art methods,the proposed method achieved much improved performance and great robustness with an average accuracy of 98.85%on CK+facial datasets,86.63%on the multi-view BU-3DEF dataset,and 57.20%on LFW in-the-wild dataset.