Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-13
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
To overcome the shortcomings of inaccurate textural direction representation and high-computational complexity of Local Binary Patterns (LBPs), we propose a novel feature descriptor named as Local Dominant Directional Symmetrical Coding Patterns (LDDSCPs).
Inspired by the directional sensitivity of human visual system, we partition eight convolution masks into two symmetrical groups according to their directions and adopt these two groups to compute the convolution values of each pixel.
Then, we encode the dominant direction information of facial expression texture by comparing each pixel’s convolution values with the average strength of its belonging group and obtain LDDSCP-1 and LDDSCP-2 codes, respectively.
At last, in view of the symmetry of two groups of direction masks, we stack these corresponding histograms of LDDSCP-1 and LDDSCP-2 codes into the ultimate LDDSCP feature vector which has effects on the more precise facial feature description and the lower computational complexity.
Experimental results on the JAFFE and Cohn-Kanade databases demonstrate that the proposed LDDSCP feature descriptor compared with LBP, Gabor, and other traditional operators achieves superior performance in recognition rate and computational complexity.
Furthermore, it is also no less inferior to some state-of-the-art local descriptors like as LDP, LDNP, es-LBP, and GDP.
American Psychological Association (APA)
Tong, Ying& Chen, Rui. 2019. Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129419
Modern Language Association (MLA)
Tong, Ying& Chen, Rui. Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1129419
American Medical Association (AMA)
Tong, Ying& Chen, Rui. Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129419
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129419