A Multiway Semi-supervised Online Sequential Extreme Learning Machine for Facial Expression Recognition with Kinect RGB-D Images

This paper aims to develop a facial expression recognition algorithm for a personal digital assistance application. Based on the Kinect RGB-D images, we propose a multiway extreme learning machine (MW-ELM) for facial expression recognition, which reduces the computing complexity significantly by processing the RGB and Depth channels separately at the input layer. Referring to our earlier work on semi-supervised online sequential extreme learning machine (SOS-ELM) that enhances the application to do the fast and incremental learning based on a few labeled samples together with some un-labeled samples of the specific user, we propose to do the parameter training with semi-supervising and on-line sequential methods for the higher hidden layer. The experiment of our proposed multiway semi-supervised online sequential extreme learning machine (MW-SOS-ELM) applying in the facial expression recognition, shows that our proposed approach achieves almost the same recognition accuracy with SOS-ELM, but reduces recognition time significantly, under the same configuration of hidden nodes. Additionally, the experiments show that our semi-supervised learning scheme reduces the requirement of labeled data sharply.