Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
Micro expressions are usually subtle and brief facial expressions that humans use to hide their true emotional states. In recent years, micro-expression recognition has attracted wide attention in the fields of psychology, mass media, and computer vision. The shortest micro expression lasts only 1/2...
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MDPI AG
2018-10-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/10/1811 |
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| author | Yue Zhao Jiancheng Xu |
| author_facet | Yue Zhao Jiancheng Xu |
| author_sort | Yue Zhao |
| collection | DOAJ |
| description | Micro expressions are usually subtle and brief facial expressions that humans use to hide their true emotional states. In recent years, micro-expression recognition has attracted wide attention in the fields of psychology, mass media, and computer vision. The shortest micro expression lasts only 1/25 s. Furthermore, different from macro-expressions, micro-expressions have considerable low intensity and inadequate contraction of the facial muscles. Based on these characteristics, automatic micro-expression detection and recognition are great challenges in the field of computer vision. In this paper, we propose a novel automatic facial expression recognition framework based on necessary morphological patches (NMPs) to better detect and identify micro expressions. Micro expression is a subconscious facial muscle response. It is not controlled by the rational thought of the brain. Therefore, it calls on a few facial muscles and has local properties. NMPs are the facial regions that must be involved when a micro expression occurs. NMPs were screened based on weighting the facial active patches instead of the holistic utilization of the entire facial area. Firstly, we manually define the active facial patches according to the facial landmark coordinates and the facial action coding system (FACS). Secondly, we use a LBP-TOP descriptor to extract features in these patches and the Entropy-Weight method to select NMP. Finally, we obtain the weighted LBP-TOP features of these NMP. We test on two recent publicly available datasets: CASME II and SMIC database that provided sufficient samples. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results. |
| first_indexed | 2024-12-13T09:37:46Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T09:37:46Z |
| publishDate | 2018-10-01 |
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| spelling | doaj.art-c715f701c3e94c80894025d094acfd5b2022-12-21T23:52:18ZengMDPI AGApplied Sciences2076-34172018-10-01810181110.3390/app8101811app8101811Necessary Morphological Patches Extraction for Automatic Micro-Expression RecognitionYue Zhao0Jiancheng Xu1School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, ChinaMicro expressions are usually subtle and brief facial expressions that humans use to hide their true emotional states. In recent years, micro-expression recognition has attracted wide attention in the fields of psychology, mass media, and computer vision. The shortest micro expression lasts only 1/25 s. Furthermore, different from macro-expressions, micro-expressions have considerable low intensity and inadequate contraction of the facial muscles. Based on these characteristics, automatic micro-expression detection and recognition are great challenges in the field of computer vision. In this paper, we propose a novel automatic facial expression recognition framework based on necessary morphological patches (NMPs) to better detect and identify micro expressions. Micro expression is a subconscious facial muscle response. It is not controlled by the rational thought of the brain. Therefore, it calls on a few facial muscles and has local properties. NMPs are the facial regions that must be involved when a micro expression occurs. NMPs were screened based on weighting the facial active patches instead of the holistic utilization of the entire facial area. Firstly, we manually define the active facial patches according to the facial landmark coordinates and the facial action coding system (FACS). Secondly, we use a LBP-TOP descriptor to extract features in these patches and the Entropy-Weight method to select NMP. Finally, we obtain the weighted LBP-TOP features of these NMP. We test on two recent publicly available datasets: CASME II and SMIC database that provided sufficient samples. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.http://www.mdpi.com/2076-3417/8/10/1811micro-expressionlocal movementnecessary morphological patches (NMPs)local binary patternfeature selection |
| spellingShingle | Yue Zhao Jiancheng Xu Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition Applied Sciences micro-expression local movement necessary morphological patches (NMPs) local binary pattern feature selection |
| title | Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition |
| title_full | Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition |
| title_fullStr | Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition |
| title_full_unstemmed | Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition |
| title_short | Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition |
| title_sort | necessary morphological patches extraction for automatic micro expression recognition |
| topic | micro-expression local movement necessary morphological patches (NMPs) local binary pattern feature selection |
| url | http://www.mdpi.com/2076-3417/8/10/1811 |
| work_keys_str_mv | AT yuezhao necessarymorphologicalpatchesextractionforautomaticmicroexpressionrecognition AT jianchengxu necessarymorphologicalpatchesextractionforautomaticmicroexpressionrecognition |