Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/874
Title: | A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering | Authors: | Ucar, Aysegul Demir, Yakup Guzelis, Cuneyt |
Keywords: | Online sequential extreme learning machine Local curvelet transform Spherical clustering Facial expression recognition Face Recognition Neural-Network Algorithm Classification |
Publisher: | Springer London Ltd | Abstract: | In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness. | URI: | https://doi.org/10.1007/s00521-014-1569-1 https://hdl.handle.net/20.500.14365/874 |
ISSN: | 0941-0643 1433-3058 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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