A Novel Approach to FER using PCA, Gabor Wavelets and LBP
Sivaiah Bellamkonda1, Gopalan N.P2, Ramachandran Vedantham3
1Sivaiah Bellamkonda, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India.
2Gopalan N.P, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India.
3Ramachandran Vedantham, Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Nambur, Guntur, India
Manuscript received on 10 December 2018 | Revised Manuscript received on 17 December 2018 | Manuscript Published on 26 December 2018 | PP: 444-448 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: ES2135017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: FER has become a hot area of research due to its inevitable role in the fields of human computer interaction, pain detection in patients, automated learning systems etc. A new FER model is proposed in this paper. First, the dimension of the input images is reduced using PCA. Then features are extracted using either Gabor wavelets or LBP. SVM classifier is used for classification. The model is evaluated on benchmark datasets such as JAFFE, Cohn-Kanade and MMI. It has been observed that this model outperforms some of the earlier ones on facial expression recognition found in literature.
Keywords: LBP Features; PCA Dimensionality Reduction; SVM Classifier; Gabor Wavelets; Computer Vision; HCI.
Scope of the Article: Communication