Journal
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Volume 23, Issue 3, Pages 444-455Publisher
FUJI TECHNOLOGY PRESS LTD
DOI: 10.20965/jaciii.2019.p0444
Keywords
facial expression recognition; 2D Gabor; LBP; ELM; human-robot interaction
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Funding
- National Natural Science Foundation of China [61403422, 61703375, 61273102]
- Hubei Provincial Natural Science Foundation of China [2018CFB447, 2015CFA010]
- Wuhan Science and Technology Project [2017010201010133]
- 111 project [B17040]
- Fundamental Research Funds for National University, China University of Geosciences (Wuhan) [1810491T07]
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The efficiency of facial expression recognition (FER) is important for human-robot interaction. Detection of the facial region, extraction of discriminative facial expression features, and identification of categories of facial expressions are all related to the recognition accuracy and time-efficiency. An FER framework is proposed, in which 2D Gabor and local binary pattern (LBP) are combined to extract discriminative features of salient facial expression patches, and extreme learning machine (ELM) is adopted to identify facial expression categories. The combination of 2D Gabor and LBP can not only describe multiscale and multidirectional textural features, but also capture small local details. The FER of ELM and support vector machine (SVM) is performed using the Japanese female facial expression database and extended Cohn-Kanade database, respectively, in which both ELM and SVM achieve an accuracy of more than 85 % , and the computational efficiency of ELM is higher than that of SVM. The proposed framework has been used in the multimodal emotional communication based humans-robots interaction system, in which FER within 2 seconds enables real-time human-robot interaction.
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