Applying Partial Differential Equations on Cubic Uniform Local Binary Pattern to Reveal Micro-Changes

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	Applying Partial Differential Equations on Cubic Uniform Local Binary Pattern to Reveal Micro-Changes
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 18، دوره 12، شماره 1، فروردین 2024، صفحه 259-270 اصل مقاله (1.28 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9600.639
نویسندگان
V. Esmaeili¹؛ M. Mohassel Feghhi^* ¹؛ S. O. Shahdi²
¹Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
²Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
تاریخ دریافت: 01 شهریور 1402، تاریخ بازنگری: 16 آبان 1402، تاریخ پذیرش: 13 آذر 1402
چکیده
Background and Objectives: The world we live in everyday, accompany with enormous numbers of minute variations which affect us and our surroundings in several aspects. These variations, so called micro-changes, are low in intensity and brief in duration which makes them almost invisible to naked eyes. Nonetheless, revealing them could open up a new wide range of applications from security, business, engineering, medical, and seismology to psychology. Methods: In this paper, we adopted a novel autonomous approach comprising Partial Differential Equations (PDE) and Cubic Uniform Local Binary Pattern (Cubic-ULBP) to spot micro-changes. Cubic-ULBP extracts 15 planes containing robust features against noise and illumination variations. Afterwards, PDE pick out single plane out of 15 to reduce time consumption. Results: The proposed method is not only optimized to get the job done perfectly but also provides promising results comparing with most state-of-the-art methods. So that the accuracy is increased about 36% and 40% on the CASME and the CASME II databases, respectively. Conclusion: The combination of the PDE and the Cubic-ULBP creates a strong and optimal method for detecting the apex frame and micro-movement. This method's performance is found to be promising when the training samples are scarce, too.
کلیدواژه‌ها
Apex؛ Cubic-ULBP؛ Micro-changes؛ Micro-expression؛ Partial Differential Equations (PDE)

مراجع
[1] P. Ekman, W. V. Friesen, "Nonverbal leakage and clues to deception," Psychiatry, 32(1): 88-106, 1969.‏ [2] B. Yang, J. Cheng, Y. Yang, B. Zhang, J. Li, "MERTA: micro-expression recognition with ternary attentions," Multimedia Tools Appl., 80(11): 1-16, 2021. [3] L. Zhou, X. Shao, Q. Mao, "A survey of micro-expression recognition," Image Vision Comput., 105: 104043, 2021.‏ [4] V. Esmaeili, S. O. Shahdi, "Automatic micro-expression apex spotting using Cubic-LBP," Multimedia Tools Appl., 79(27): 20221-20239, 2020. [5] V. Esmaeili, M. M. Feghhi, S. O. Shahdi, "Micro-Expression recognition using histogram of image gradient orientation on diagonal planes," in Proc. 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA): 1-5, 2021. [6] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "Autonomous apex detection and micro-expression recognition using proposed diagonal planes," Int. J. Nonlinear Anal. Appl., 11: 483-497,‏ 2020. [7] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "A comprehensive survey on facial micro-expression: approaches and databases," Multimedia Tools Appl., 81: 40089- 40134, 2022. [8] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "Automatic micro-expression apex frame spotting using local binary pattern from six intersection planes," arXiv preprint arXiv: 2104.02149, 2021.‏ [9] M. Xiao, Y. Chen, J. Zhu, H. Zhang, X. Zhao, L. Gao, W. Xing, "Climbing the apex of the ORR volcano plot via binuclear site construction: electronic and geometric engineering," J. the Am. Chem. Soc., 141(44): 17763-17770,‏ 2019. [10] K. S. Exner, "Does a thermoneutral electrocatalyst correspond to the apex of a volcano plot for a simple two‐electron process?," Angew. Chem. Int. Ed., 59(26): 10236-10240, 2020. [11] C. Widiwijayanti, J. Déverchère, R. Louat, M. Sébrier, H. Harjono, M. Diament, D. Hidayat, "Aftershock sequence of the 1994, Mw 6.8, Liwa earthquake (Indonesia): Seismic rupture process in a volcanic arc," Geophys. Res. Lett., 23(21): 3051-3054,‏ 1996. [12] J. Skinner, "The smoke of an eruption and the dust of an earthquake: Dark tourism, the sublime, and the re-animation of the disaster location," in the Palgrave handbook of dark tourism studies. Palgrave Macmillan, London: 125-150,‏ 2018. [13] O. Yildirim, M. Talo, E. J. Ciaccio, R. San Tan, U. R. Acharya, "Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records," Comput. Methods Programs Biomed., 197: 105740,‏ 2020. [14] E. Moghadas, J. Rezazadeh, R. Farahbakhsh, "An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase," Internet Things, 11: 100251,‏ 2020. [15] S. T. Liong, J. See, K. Wong, R. C. W. Phan, "Less is more: Micro-expression recognition from video using apex frame," Signal Process. Image Commun., 62: 82-92,‏ 2018. [16] Y. Li, X. Huang, G. Zhao, "Can micro-expression be recognized based on single apex frame?," in Proc. 2018 25th IEEE International Conference on Image Processing (ICIP): 3094-3098,‏ 2018. [17] G. Zhao, M. Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE Trans. Pattern Anal. Mach. Intell., 29(6): 915-928,‏ 2007. [18] T. Ojala, M. Pietikäinen, D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recogn., 29(1): 51-59,‏ 1996. [19] T. Ojala, M. Pietikainen, T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., 24(7): 971-987,‏ 2002. [20] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "Micro-expression recognition based on the multi-color ulbp and histogram of gradient direction from six intersection planes," J. Iran. Associ. Electr. Electron. Eng., 19(3): 123-130, 2022. [21] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "Spotting micro‐movements in image sequence by introducing intelligent cubic‐LBP," IET Image Process., 16(14): 3814-3830, 2022.‏ [22] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "Early COVID-19 diagnosis from lung ultrasound images combining RIULBP-TP and 3D-DenseNet," in Proc. 2022 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS): 1-5, 2022. [23] V. Esmaeili, M. Mohassel Feghhi, "Real-time authentication for electronic service applicants using a method based on two-stream 3d deep learning," Soft Comput. J., 2023. [24] V. Esmaeili, M. Mohassel Feghhi, "Diagnosis of Covid-19 disease by combining hand-crafted and deep-learning methods on ultrasound data," J. Mach. Vision Image Process., 9(4): 31-41, 2022. [25] V. Esmaeili, M. Mohassel Feghhi, "COVID-19 diagnosis: ULBPFP-net approach on lung ultrasound data," Iran. J. Electr. Electron. Eng., 19(3): 2586-2586, 2023. [26] V. Esmaeili, M. Mohassel Feghhi, S. O. Shahdi, "Automatic micro-expression recognition using LBP-SIPl and FR-CNN," AUT J. Model. Simul., 54(1): 59-72, 2022. [27] N. Wadhwa, M. Rubinstein, F. Durand, W. T. Freeman, "Phase-based video motion processing," ACM Trans. Graphics (TOG), 32(4): 1-10, 2013. [28] H. Y. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, W. Freeman, "Eulerian video magnification for revealing subtle changes in the world," ACM Trans. Graphics (TOG), 31(4): 1-8, 2012. [29] G. Fahmy, M. F. Fahmy, O. M. Fahmy, "Micro-movement magnification in video signals using complex wavelet analysis," IET Image Process., 11(11): 986-993,‏ 2017. [30] C. Liu, A. Torralba, W. T. Freeman, F. Durand, E. H. Adelson, "Motion magnification," ACM Trans. Graphics (TOG), 24(3): 519-526,‏ 2005. [31] M. Janatka, A. Sridhar, J. Kelly, D. Stoyanov, "Higher order of motion magnification for vessel localisation in surgical video," in Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention: 307-314, 2018. [32] X. Li, X. Hong, A. Moilanen, X. Huang, T. Pfister, G. Zhao, M. Pietikäinen, "Towards reading hidden emotions: A comparative study of spontaneous micro-expression spotting and recognition methods," IEEE Trans. Affective Comput., 9(4): 563-577, 2017. [33] M. Lu, Y. Chai, Q. Liu, "Observation of tower vibration based on subtle motion magnification," IFAC-PapersOnLine, 52(24): 346-350, 2019. [34] W. Fan, Z. Zheng, W. Zeng, Y. Chen, H. Q. Zeng, H. Shi, X. Luo, "Robotically surgical vessel localization using robust hybrid video motion magnification," IEEE Rob. Autom. Lett., 6(2): 1567-1573, 2021. [35] A. Mohseni, "A new PDE-based resolution enhancement technique for the analysis of low SNR particle displacement images," Eur. J. Mech. B.Fluids, 85: 289-311,‏ 2021. [36] T. Barbu, "Feature keypoint-based image compression technique using a well-posed nonlinear fourth-order PDE-based model," Mathematics, 8(6): 930,‏ 2020. [37] L. Afraites, A. Hadri, A. Laghrib, M. Nachaoui, "A high order PDE-constrained optimization for the image denoising problem," Inverse Probl. Sci. Eng., 29(12):1821-1863, ,‏ 2021. [38] A. K. Al-Jaberi, E. M. Hameed, "Topological data analysis for evaluating PDE-based denoising models," J. Phys. Conf. Ser., 1897(1): 012006. 2021. [39] C. Fang, Z. Zhao, P. Zhou, Z. Lin, "Feature learning via partial differential equation with applications to face recognition," Pattern Recogn., 69: 14-25, 2017. [40] S. Moorthi, S. Karthikeyan, "A Study On Learning Partial Differential Equations Algorithm For Face Recognition," Int. J. Innovative Res. Explorer, 5(4): 127-134, 2018. [41] W. J. Yan, Q. Wu, Y. J. Liu, S. J. Wang, X. Fu, "CASME database: A dataset of spontaneous micro-expressions collected from neutralized faces," in Proc. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG): 1-7, 2013. [42] W. J. Yan, X. Li, S. J. Wang, G. Zhao, Y. J. Liu, Y. H. Chen, X. Fu, "CASME II: An improved spontaneous micro-expression database and the baseline evaluation," PloS one, 9(1): e86041, 2014. [43] A. J. Witsil, J. B. Johnson, "Volcano video data characterized and classified using computer vision and machine learning algorithms," Geosci. Front., 11(5): 1789-1803,‏ 2020. [44] W. Fuhl, T. Kübler, K. Sippel, W. Rosenstiel, E. Kasneci, "Excuse: Robust pupil detection in real-world scenarios," in Proc. International Conference on Computer Analysis of Images and Patterns: 39-51, 2015. [45]C. Nicholson, "Evaluation metrics for machine learning—accuracy, precision, recall, and F1 defined," ed: Pathmind. http://pathmind. com/wiki/accuracy-precision-recall-f1, 2019.
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