Abstract
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.
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Acknowledgements
This research is partly supported by the National Nature Science Foundation of China (Nos. 91546111, 61672070, 61672071 and 61650201), Beijing Municipal Natural Science Foundation (4152005, 4162058), Key project of Beijing Municipal Education Commission (No. KZ201610005009).
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Jia, X., Chen, X., Miao, J. (2017). A Multiway Semi-supervised Online Sequential Extreme Learning Machine for Facial Expression Recognition with Kinect RGB-D Images. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_22
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DOI: https://doi.org/10.1007/978-3-319-63312-1_22
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