Facial expression recognition based on AAM-SIFT and adaptive regional weighting
The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non‐frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognitio...
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| Published in: | IEEJ transactions on electrical and electronic engineering Vol. 10; no. 6; pp. 713 - 722 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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01.11.2015
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| ISSN: | 1931-4973, 1931-4981 |
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| Abstract | The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non‐frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognition based on AAM‐SIFT and adaptive regional weighting. First, multi‐pose AAM templates are used for pose estimation and feature point location of the facial expression image. For effective and efficient description of these feature points, a hybrid representation, which integrates gradient direction histograms based on the descriptors of scale‐invariant feature transform (SIFT) and AAM, is utilized to form AAM‐SIFT features. Meanwhile, according to different expression regions, AAM‐SIFT features are divided into different groups and the obtained adaptive weights by means of a regional weighted method based on the fuzzy C‐means (FCM) clustering algorithm. Finally, the membership degree computed by FCM, which represents the possibility for each class, is regarded as the input feature vector for support vector machine (SVM) classifier. Extensive experiments on BU‐3DFE database with six facial expressions and seven poses demonstrate the effectiveness of different types of weighting strategies and the influence of different features. Comparison with other state‐of‐art methods illustrates that the proposed method not only improves the recognition rates of the frontal face but also has better robustness for non‐frontal facial expressions. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
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| AbstractList | The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non‐frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognition based on AAM‐SIFT and adaptive regional weighting. First, multi‐pose AAM templates are used for pose estimation and feature point location of the facial expression image. For effective and efficient description of these feature points, a hybrid representation, which integrates gradient direction histograms based on the descriptors of scale‐invariant feature transform (SIFT) and AAM, is utilized to form AAM‐SIFT features. Meanwhile, according to different expression regions, AAM‐SIFT features are divided into different groups and the obtained adaptive weights by means of a regional weighted method based on the fuzzy C‐means (FCM) clustering algorithm. Finally, the membership degree computed by FCM, which represents the possibility for each class, is regarded as the input feature vector for support vector machine (SVM) classifier. Extensive experiments on BU‐3DFE database with six facial expressions and seven poses demonstrate the effectiveness of different types of weighting strategies and the influence of different features. Comparison with other state‐of‐art methods illustrates that the proposed method not only improves the recognition rates of the frontal face but also has better robustness for non‐frontal facial expressions. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. The active appearance model (AAM), one of the most effective facial feature localization methods, is widely used in frontal facial expression recognition. However, non-frontal facial expression recognition is important in many scenarios. Thus, we propose a new method for facial expression recognition based on AAM-SIFT and adaptive regional weighting. First, multi-pose AAM templates are used for pose estimation and feature point location of the facial expression image. For effective and efficient description of these feature points, a hybrid representation, which integrates gradient direction histograms based on the descriptors of scale-invariant feature transform (SIFT) and AAM, is utilized to form AAM-SIFT features. Meanwhile, according to different expression regions, AAM-SIFT features are divided into different groups and the obtained adaptive weights by means of a regional weighted method based on the fuzzy C-means (FCM) clustering algorithm. Finally, the membership degree computed by FCM, which represents the possibility for each class, is regarded as the input feature vector for support vector machine (SVM) classifier. Extensive experiments on BU-3DFE database with six facial expressions and seven poses demonstrate the effectiveness of different types of weighting strategies and the influence of different features. Comparison with other state-of-art methods illustrates that the proposed method not only improves the recognition rates of the frontal face but also has better robustness for non-frontal facial expressions. copyright 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
| Author | Ren, Fuji Huang, Zhong |
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| Copyright | 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
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| References_xml | – reference: Cheon Y, Kim D. Natural facial expression recognition using differential-AAM and manifold learning. Journal of Pattern Recognition 2009; 42(1):1340-1350. – reference: Wang J, Yin L, Wei X, Sun Y. 3D facial expression recognition based on primitive surface feature distribution. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2006,) 2006; 2:1399-1406. – reference: Cootes T, Edwards G, Taylor C. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001; 23(6):681-685. – reference: Liu J, Liu B, Zhang S, Yang F, Yang P, Metaxas D, Neidle C. Non-manual grammatical marker recognition based on multi-scale, spatio-temporal analysis of head pose and facial expressions. Journal of Image and Vision Computing 2014; 32(10):671-681. – reference: Yu K, Wang Z, Hagenbuchner M, Feng D. Spectral embedding based facial expression recognition with multiple features. Journal of Neurocomputing 2014; 129:136-145. – reference: Tariq U, Yang J, Huang T-S. Multi-view facial expression recognition analysis with generic sparse coding feature, computer. Journal of Vision-ECCV 2012. Workshops and Demonstrations Lecture Notes in Computer Science 2012; 7585:578-588. – reference: Lowe DG. Distinctive image features from scale-invariant key points. International Journal of Computer Vision 2004; 60(2):91-110. – reference: Soladié C, Stoiber N, Séguier R. Invariant representation of facial expressions for blended expression recognition on unknown subjects. Journal of Computer Vision and Image Understanding 2013; 117(7):1598-1609. – reference: Friguid H, Nasraoui O. Unsupervised learning of prototypes and attribute weights. Journal of Pattern Recognition 2004; 37(3):576-581. – reference: Zavaschi T-H, Britto A-S, Oliveira L-E, Koerich A-L. Fusion of feature sets and classifiers for facial expression recognition. Expert Systems with Applications 2013; 40(2):646-655. – reference: Gu W, Xiang C, Venkatesh YV, Huang D, Lin H. Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Journal of Pattern Recognition 45(1):80-91. – reference: Alitappeh RJ, Saravi KJ, Mahmoudi F. A new illumination invariant feature based on SIFT descriptor in color space. Journal of Procedia Engineering 2012; 41:305-311. – reference: Tong Y, Wang Y, Zhu ZW, Ji Q. Robust facial feature tracking under varying face pose and facial expression. Journal of Pattern Recognition 2007; 40(11):3195-3208. – reference: Dornaika F, Moujahid A, Raducanu B. Facial expression recognition using tracked facial actions: classifier performance analysis. Journal of Engineering Applications of Artificial Intelligence 2013; 26(1):467-477. – reference: Rudovic O, Pantic M, Patras L. Coupled gaussian processes for pose-Invariant facial expression recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2013; 35(6):1357-1369. – reference: Ptucha R, Savakis A. Manifold based sparse representation for facial understanding in natural images. Journal of Image and Vision Computing 2013; 31(5):365-378. – reference: Soyel H, Demirel H. Localized discriminative scale invariant feature transform based facial expression recognition. Journal of Computers and Electrical Engineering 2012; 38(5):1299-1309. – reference: Wu YW, Liu H, Zha HB. Facial expression recognition by weighted clustering of grouped features. Journal of Computer-aided Design and Computer Graphics 2005; 17(11):2394-2401. – reference: Berretti S, Amor B, Daoudi M, Bimbo A. 3D facial expression recognition using SIFT descriptors of automatically detected keypoints. Journal of Visual Computer 2011; 27(11):1021-1036. – reference: Khan RA, Meyer A, Konik H. Framework for reliable, real-time facial expression recognition for low resolution images. Journal of Pattern Recognition Letters 2013; 34(10):1159-1168. – reference: Wan S, Aggarwal J-K. Spontaneous facial expression recognition: a robust metric learning approach. Journal of Pattern Recognition 2014; 47(5):1859-1868. – reference: Xie X, Lam K-M. Facial expression recognition based on shape and texture. Journal of Pattern Recognition 42(5):1003-1011. – reference: Moore S, Bowden R. Local binary patterns for multi-view facial expression recognition. Journal of Computer Vision and Image Understanding 2011; 115(4):241-558. – reference: Ren F. From cloud computing to language engineering, affective computing and advanced intelligence. International Journal of Advanced Intelligence 2010; 2(1):1-14. – reference: Zhu L, Chuang F, Wang S. Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Transactions on System, Man, and Cybernetics, Part B, Cybernetics 2009; 39(3):578-591. – reference: Cootes TF, Wheeler GV, Walker KN, Taylor CJ. View-based active appearance models. Journal of Image and vision computing 2002; 20(9):657-664. – reference: Liao K, Liu G, Hui Y. An improvement to the SIFT descriptor for image representation and matching. 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expression recognition publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 40 start-page: 646 issue: 2 year: 2013 end-page: 655 article-title: Fusion of feature sets and classifiers for facial expression recognition publication-title: Expert Systems with Applications – volume: 34 start-page: 1159 issue: 10 year: 2013 end-page: 1168 article-title: Framework for reliable, real‐time facial expression recognition for low resolution images publication-title: Journal of Pattern Recognition Letters – volume: 34 start-page: 1211 issue: 11 year: 2013 end-page: 1220 article-title: An improvement to the SIFT descriptor for image representation and matching publication-title: Journal of Pattern Recognition Letters – volume: 2 start-page: 1 issue: 1 year: 2010 end-page: 14 article-title: From cloud computing to language engineering, affective computing and advanced intelligence publication-title: International Journal of Advanced Intelligence – start-page: 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| SubjectTerms | Algorithms appearance model Face recognition Facial Fuzzy fuzzy c-means clustering gradient direction histogram non-frontal facial image Regional scale-invariant feature transform Support vector machines Weighting |
| Title | Facial expression recognition based on AAM-SIFT and adaptive regional weighting |
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