Menuju Pengenalan Ekspresi Mikro: Pendeteksian Komponen Wajah Menggunakan Discriminative Response Map Fitting

Ulla Delfana Rosiani, Priska Choirina, Surya Sumpeno, Mauridhy Hery P.


The observations made in the study of micro-expression are to recognize and track the very subtle movements of certain facial areas and in a short time. In this study, the observation of movement is held in some areas of the face component. The facial and facial components detection is the pre-process stage on micro-expression recognition system. The goal at this stage is to get face and face components accurately and quickly on every movement of the video sequence or image sequence. The face landmark point of the Discriminative Response Map Fitting (DRMF) method can be used to get face components area accurately and quickly. This can be done because the facial landmark points used in this model-based method do not change when objects are moved, rotated, or scaled. The results obtained by using this method are accurate with a 100% accuracy value compared to the Haar Cascade Classifier method with an average accuracy of 44%. In addition, the average time required in the formation of facial component boxes for each frame is 0.08 seconds, faster than the Haar Cascade Classifier method of 0.32 seconds. With the results obtained, then the detection of facial components can be obtained accurately and quickly. Furthermore, the boxes of face components obtained are expected to display the appropriate data to be processed correctly and accurately in the next stage, feature extraction and the classification of micro-expression motion stage.


deteksi komponen wajah, DRMF, titik landmark wajah, ekspresi mikro

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R. Wiryadinata, U. Istiyah, R. Fahrizal, P. Priswanto, dan S. Wardoyo, “Sistem Presensi Menggunakan Algoritme Eigenface dengan Deteksi Aksesoris dan Ekspresi Wajah,” J. Nas. Tek. Elektro Dan Teknol. Inf. JNTETI, Vol. 6, No. 2, hal. 222-229, 2017.

H. Pratikno, “Kontrol Gerakan Objek 3D Augmented Reality Berbasis Titik Fitur Wajah dengan POSIT,” J. Nas. Tek. Elektro Dan Teknol. Inf. JNTETI, Vol. 4, No. 1, hal. 16-24, 2015.

U. D. Rosiani, A. Atmoko, S. Sumpeno, dan M. H. Purnomo, “The Synthesis of Javanese Woman’s Facial Image on Anger Expression Based on Emotion Regulation,” 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2015, hal. 185–190.

W.-J. Yan dkk., “CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation,” PLoS ONE, Vol. 9, No. 1, hal. 1-8, Jan 2014.

S. L. Happy dan A. Routray, “Fuzzy Histogram of Optical Flow Orientations for Micro-expression Recognition,” IEEE Transactions on Affective Computing (Early Access), Vol. PP, No. 99, hal. 1–1, 2017.

M. Chen, H. T. Ma, J. Li, dan H. Wang, “Emotion Recognition Using Fixed Length Micro-Expressions Sequence and Weighting Method,” IEEE International Conference on Real-time Computing and Robotics (RCAR), 2016, hal. 427–430.

W.-J. Yan, Q. Wu, J. Liang, Y.-H. Chen, dan X. Fu, “How Fast are the Leaked Facial Expressions: The Duration of Micro-Expressions,” J. Nonverbal Behav., Vol. 37, No. 4, hal. 217–230, Des 2013.

X. Huang, G. Zhao, X. Hong, W. Zheng, dan M. Pietikäinen, “Spontaneous Facial Micro-Expression Analysis Using Spatiotemporal Completed Local Quantized Patterns,” Neurocomputing, Vol. 175, Part A, hal. 564–578, Jan 2016.

X. Li dkk., “Reading Hidden Emotions: Spontaneous Micro-Expression Spotting and Recognition,” ArXiv Prepr. ArXiv151100423, 2015.

D. Patel, G. Zhao, dan M. Pietikäinen, “Spatiotemporal Integration of Optical Flow Vectors for Micro-Expression Detection,” International Conference on Advanced Concepts for Intelligent Vision Systems, 2015, hal. 369–380.

S.-T. Liong, J. See, R. C.-W. Phan, K. Wong, dan S.-W. Tan, “Hybrid Facial Regions Extraction for Micro-expression Recognition System,” J. Signal Process. Syst., Vol. 90, Issue 4, hal. 1–17, 2017.

A. Asthana, S. Zafeiriou, S. Cheng, dan M. Pantic, “Robust Discriminative Response Map Fitting with Constrained Local Models,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, hlm. 3444–3451.

D. Cristinacce dan T. F. Cootes, “Feature Detection and Tracking with Constrained Local Models,” Proceedings of the British Machine Vision Conference 2006, 2006, hal. 929-938.

A. Asthana, S. Zafeiriou, S. Cheng, dan M. Pantic, “Incremental Face Alignment in the Wild,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, hal. 1859–1866.

M. D. Putro, T. B. Adji, dan B. Winduratna, “Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones,” Seminar Nasional “Science, Engineering and Technology”– 2012, 2012, hal. TIF09-1.

H. L. Chin, M. Hanafi, dan T. D. Salka, “Integrated Face and Facial Components Detection,” 2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim), 2015, hal. 87–91.

P. Ekman dan W. V. Friesen, “Nonverbal Leakage and Clues to Deception,” Psychiatry, Vol. 32, No. 1, hal. 88–106, 1969.

S. Porter dan L. ten Brinke, “Reading Between the Lies: Identifying Concealed and Falsified Emotions in Universal Facial Expressions,” Psychol. Sci., Vol. 19, No. 5, hal. 508–514, Mei 2008.

X. Ben, P. Zhang, R. Yan, M. Yang, dan G. Ge, “Gait Recognition and Micro-Expression Recognition Based on Maximum Margin Projection with Tensor Representation,” Neural Comput. Appl., Vol. 27, No. 8, hal. 2629–2646, Nov 2016.

N. H. Frijda, The Emotions. Cambridge University Press, 1986.

P. Zhang, X. Ben, R. Yan, C. Wu, dan C. Guo, “Micro-expression recognition system,” Opt. - Int. J. Light Electron Opt., Vol. 127, No. 3, hal. 1395–1400, Feb 2016.

P. Viola dan M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, hal. I.511-I.518.

R. Lienhart dan J. Maydt, “An Extended Set of Haar-like Features for Rapid Object Detection,” Proceedings of 2002 International Conference on Image Processing, 2002, hal. I.900–I.903.

J. M. Saragih, S. Lucey, dan J. F. Cohn, “Deformable Model Fitting by Regularized Landmark Mean-shift,” Int. J. Comput. Vis., Vol. 91, No. 2, hal. 200–215, 2011.

X. Zhu dan D. Ramanan, “Face Detection, Pose Estimation, and Landmark Localization in the Wild,” 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, hal. 2879–2886.

N. Dalal dan B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), 2005, hal. 886–893.

M. Sokolova dan G. Lapalme, “A Systematic Analysis of Performance Measures for Classification Tasks,” Inf. Process. Manag., Vol. 45, No. 4, hal. 427–437, 2009.



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