Micro Expression Recognition Using Delaunay Triangulation and Voronoi Tessellation

REFERENCES

• M. Dailey, C. Joyce, M. Lyons, M. Kamachi, H. Ishi, J. Gyoba, and G. Cottrell, “Evidence and a computational explanation of cultural differences in facial expression recognition,” Emotion, Vol. 10, pp. 874–893, 2010.
   PubMed Web of Science ®Google Scholar
• J. Russell, “Culture and the categorization of emotions,” Psychol. Bull., Vol. 110, pp. 426–450, 1991.
   PubMed Web of Science ®Google Scholar
• M. Sultan Zia, M. Hussain, and M. Arfan Jaffar, “A novel spontaneous facial expression recognition using dynamically weighted majority voting based ensemble classifier,” Multimed. Tools. Appl., Vol. 77, pp. 25537–25567, 2018.
   Web of Science ®Google Scholar
• J. Cohn, Z. Ambadar, and P. Ekman, “Observer- based measurement of facial expression with the facial action coding system,” in The Handbook of Emotion Elicitation and Assessment, Oxford University Press Series in Affective Science, J. A. Coan, and J. J. B. Allen, Ed. New York, NY: Oxford University, 2007, pp. 203–221.
   Google Scholar
• M. Valstar, M. Mehu, B. Jiang, M. Pantic, and K. Scherer, “Meta-analysis of the first facial expression recognition challenge,” IEEE Trans. Syst. Man Cybern. B Cybern., Vol. 42, pp. 966–979, 2012.
   PubMed Web of Science ®Google Scholar
• B. Martinez, M. Valstar, B. Jiang, and M. Pantic, “Automatic analysis of facial actions: A survey,” IEEE Trans. Affect. Comput., Vol. 13, pp. 1–22, 2017.
   Google Scholar
• P. Ekman, “Body position, facial expression, and verbal behavior during interviews,” J. Abnorm. Soc. Psychol., Vol. 68, pp. 295–301, 1964.
   PubMed Web of Science ®Google Scholar
• R. Zhi, M. Liu, and D. Zhang, “A comprehensive survey on automatic facial action unit analysis,” Vis. Comput., Vol. 36, pp. 1067–1093, 2020.
   Web of Science ®Google Scholar
• E. Friesen, and P. Ekman, “Facial action coding system: a technique for the measurement of facial movement,” Palo Alto, Vol. 3, p. 5, 1978.
   Google Scholar
• R. Zhi, C. Zhou, T. Li, S. Liu, and Y. Jin, “Action unit analysis enhanced facial expression recognition by deep neural network evolution,” Neuro-computing, Vol. 425, pp. 135–148, 2021.
   Web of Science ®Google Scholar
• L. Barrett, R. Adolphs, S. Marsella, A. Martinez, and S. Pollak, “Emotional expressions reconsidered: challenges to inferring emotion from human facial movements,” Psychol. Sci. Public. Interest., Vol. 20, pp. 1–68, 2019.
   PubMed Web of Science ®Google Scholar
• S. Liong, J. See, K. Wong, and R. Phan, “Less is more: micro-expression recognition from video using apex frame,” Signal Process., Image Commun., Vol. 62, pp. 82–92, 2018.
   Web of Science ®Google Scholar
• J. He, X. Yu, B. Sun, and L. Yu, “Facial expression and action unit recognition augmented by their dependencies on graph convolutional networks,” J. Multimodal User Interfaces, Vol. 15, pp. 429–440, 2021.
   Web of Science ®Google Scholar
• H. Xie, L. Lo, H. Shuai, and W. Cheng, “Au- assisted graph attention convolutional network for micro-expression recognition,” in Proceedings of The 28th ACM International Conference On Multimedia, 2020, pp. 2871–2880.
   Google Scholar
• Y. Oh, J. See, A. Le Ngo, R. Phan, and V. Baskaran, “A survey of automatic facial micro-expression analysis: databases, methods, and challenges,” Front. Psychol., Vol. 9, p. 1128, 2018.
   PubMed Web of Science ®Google Scholar
• S. Wang, W. Yan, X. Li, G. Zhao, and X. Fu, “Micro-expression recognition using dynamic textures on tensor independent color space,” in 2014 22nd International Conference on Pattern Recognition, 2014, pp. 4678–4683.
   Google Scholar
• N. Thi Thu Nguyen, D. Thi Thu Nguyen, and B. The Pham, “Micro-expression recognition based on the fusion between optical flow and dynamic image,” in 2021 The 5th International Conference on Machine Learning And Soft Computing, 2021, pp. 115–120.
   Google Scholar
• S. Happy, and A. Routray, “Automatic facial expression recognition using features of salient facial patches,” IEEE Trans. Affect. Comput., Vol. 6, pp. 1–12, 2015.
   Web of Science ®Google Scholar
• H. Khor, J. See, S. Liong, R. Phan, and W. Lin, “Dual-stream shallow networks for facial micro-expression recognition,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 36–40.
   Google Scholar
• S. Liong, and K. Wong, “Micro-expression recognition using apex frame with phase information,” in 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017, pp. 534–537.
   Google Scholar
• I. Kotsia, N. Nikolaidis, and I. Pitas, “Fusion of geometrical and texture information for facial expression recognition,” in 2006 International Conference on Image Processing, 2006, pp. 2649–2652.
   Google Scholar
• Y. Tang, X. Zhang, and H. Wang, “Geometric- convolutional feature fusion based on learning propagation for facial expression recognition,” IEEE. Access., Vol. 6, pp. 42532–42540, 2018.
   Web of Science ®Google Scholar
• Y. Fan, J. Lam, and V. Li, “Multi-region ensemble convolutional neural network for facial expression recognition,” in International Conference on Artificial Neural Networks, 2018, pp. 84–94.
   Google Scholar
• Y. Li, X. Huang, and G. Zhao, “Micro-expression action unit detection with spatial and channel attention,” Neurocomputing, Vol. 436, pp. 221–231, 2021.
   Web of Science ®Google Scholar
• Z. Lu, Z. Luo, H. Zheng, J. Chen, and W. Li, “A delaunay-based temporal coding model for micro-expression recognition,” in Asian Conference on Computer Vision, 2014, pp. 698–711.
   Google Scholar
• Y. Zhang, Y. Liu, and H. Wang, “Cross-database micro-expression recognition exploiting intradomain structure,” J. Healthc. Eng., Vol. 2021, pp. 1–9, 2021.
   Web of Science ®Google Scholar
• W. Yan, X. Li, S. Wang, G. Zhao, Y. Liu, Y. Chen, and X. Fu, “CASME II: An improved spontaneous micro-expression database and the baseline evaluation,” PloS One, Vol. 9, p. e86041, 2014.
   PubMed Web of Science ®Google Scholar
• A. Davison, C. Lansley, N. Costen, K. Tan, and M. Yap, “Samm: A spontaneous micro-facial movement dataset,” IEEE Trans. Affect. Comput., Vol. 9, pp. 116–129, 2018.
   Web of Science ®Google Scholar
• X. Li, T. Pfister, X. Huang, G. Zhao, and M. Pietikinen, “A spontaneous micro-expression database: inducement, collection and baseline,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, 2013, pp. 1–6.
   Google Scholar
• T. Pfister, X. Li, G. Zhao, and M. Pietikinen, “Recognising spontaneous facial micro- expressions,” in 2011 International Conference On Computer Vision, 2011, pp. 1449–1456.
   Google Scholar
• J. Khodadoust, and A. Khodadoust, “Fingerprint indexing based on expanded Delaunay triangulation,” Expert. Syst. Appl., Vol. 81, pp. 251–267, 2017.
   Web of Science ®Google Scholar
• S. Jiang, and W. Jiang, “Reliable image matching via photometric and geometric constraints structured by Delaunay triangulation,” ISPRS. J. Photogramm. Remote. Sens., Vol. 153, pp. 1–20, 2019.
   Web of Science ®Google Scholar
• B. Delaunay, et al., “Sur la sphere vide,” Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii I Estestvennyka Nauk, Vol. 7, pp. 1–2, 1934.
   Google Scholar
• A. Cheddad, D. Mohamad, and A. Abd Manaf, “Exploiting Voronoi diagram properties in face segmentation and feature extraction,” Pattern Recognit., Vol. 41, pp. 3842–3859, 2008.
   Web of Science ®Google Scholar
• G. Bebis, T. Deaconu, and M. Georgiopoulos, “Fingerprint identification using delaunay triangulation,” in Proceedings 1999 International Conference On Information Intelligence And Systems (Cat. No. PR00446), 1999, pp. 452–459.
   Google Scholar
• K. Liu, Q. Jin, H. Xu, Y. Gan, and S. Liong, “Micro-expression recognition using advanced genetic algorithm,” Signal Process., Image Commun., Vol. 93, p. 116153, 2021.
   Web of Science ®Google Scholar
• M. Munasinghe, “Facial expression recognition using facial landmarks and random forest classifier,” in 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018, pp. 423–427.
   Google Scholar
• H. Shahar, and H. Hel-Or, “Micro Expression classification using facial color and deep learning methods,” in Proceedings of The IEEE/CVF International Conference On Computer Vision Work-Shops, 2019, pp. 0–0.
   Google Scholar
• Y. Liu, X. Zhang, Y. Lin, and H. Wang, “Facial expression recognition via deep action units graph network based on psychological mechanism,” IEEE Trans. Cogn. Dev. Syst., Vol. 12, pp. 311–322, 2020.
   Web of Science ®Google Scholar
• A. Majumder, L. Behera, and V. Subramanian, “Automatic facial expression recognition system using deep network-based data fusion,” IEEE Trans. Cybern., Vol. 48, pp. 103–114, 2018.
   PubMed Web of Science ®Google Scholar
• H. Ghazouani, “A genetic programming-based feature selection and fusion for facial expression recognition,” Appl. Soft. Comput., Vol. 103, p. 107173, 2021.
   Web of Science ®Google Scholar
• T. Ojala, M. Pietikinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognit., Vol. 29, pp. 51–59, 1996.
   Web of Science ®Google Scholar
• T. Senechal, K. Bailly, and L. Prevost, “Impact of action unit detection in automatic emotion recognition,” Pattern. Anal. Appl., Vol. 17, pp. 51–67, 2014.
   Web of Science ®Google Scholar
• L. Lei, J. Li, T. Chen, and S. Li, “A novel graph-TCN with a graph structured representation for micro-expression recognition,” in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 2237–2245.
   Google Scholar
• S. Wang, C. Wu, M. He, J. Wang, and Q. Ji, “Posed and spontaneous expression recognition through modeling their spatial patterns,” Mach. Vis. Appl., Vol. 26, pp. 219–231, 2015.
   Web of Science ®Google Scholar
• C. Aggarwal. Data classification. Data Mining. 2015, pp. 285–344.
   Google Scholar
• G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. Spyropoulos, and P. Stamatopoulos. Stacking classifiers for anti-spam filtering of e-mail. ArXiv Preprint Cs/0106040. (2001).
   Google Scholar
• S. Malmasi, and M. Dras, “Native language identification with classifier stacking and ensembles,” Comput. Linguist., Vol. 44, pp. 403–446, 2018.
   Web of Science ®Google Scholar
• S. Safavian, and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans. Syst. Man Cybern., Vol. 21, pp. 660–674, 1991.
   Web of Science ®Google Scholar
• Y. Yaddaden, M. Adda, A. Bouzouane, S. Gaboury, and B. Bouchard, “Hybrid-based facial expression recognition approach for human-computer interaction,” in 2018 IEEE 20th International Workshop On Multimedia Signal Processing (MMSP), 2018, pp. 1–6.
   Google Scholar
• H. Dino, and M. Abdulrazzaq, “Facial expression classification based on SVM, KNN and MLP classifiers,” in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 70–75.
   Google Scholar
• A. McCallum, K. Nigam, “A comparison of event models for naive bayes text classification,” in AAAI-98 Workshop on Learning for Text Categorization. Vol. 752, 1998, pp. 41–48.
   Google Scholar
• R. Mihalcea. Classifier stacking and voting for text filtering. TREC. 2002.
   Google Scholar
• W. Li, and L. Zou, “Classifier stacking for native language identification,” in Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, 2017, pp. 390–397.
   Google Scholar
• A. Kumar, R. Theagarajan, O. Peraza, and B. Bhanu, “Classification of facial micro-expressions using motion magnified emotion avatar images,” in CVPR Workshops, 2019, pp. 12–20.
   Google Scholar
• M. Takalkar, S. Thuseethan, S. Rajasegarar, Z. Chaczko, M. Xu, and J. Yearwood, “LGAttnet: automatic micro-expression detection using dual-stream local and global attentions,” Knowl. Based. Syst., Vol. 212, p. 106566, 2021.
   Web of Science ®Google Scholar
• Y. Zhang, Y. Liu, G. Li, and H. Peng, “Sub- space learning and joint distribution Adaptation for unsupervised cross-database microexpression recognition,” Mob. Inf. Syst., Vol. 2021, pp. 1–8, 2021.
   Web of Science ®Google Scholar
• M. Peng, C. Wang, T. Bi, Y. Shi, X. Zhou, and T. Chen, “A novel apex-time network for cross-dataset micro-expression recognition,” in 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), 2019, pp. 1–6.
   Google Scholar
• Y. Zong, W. Zheng, X. Huang, J. Shi, Z. Cui, and G. Zhao, “Domain regeneration for cross-database micro-expression recognition,” IEEE Trans. Image Process., Vol. 27, pp. 2484–2498, 2018.
   Web of Science ®Google Scholar