Recognition of Emotions through Facial Geometry with Normalized Landmarks
Resumo
Emotion recognition holds pivotal significance in human social interactions, as it entails the discernment of facial patterns intricately linked to diverse emotional states. The scientific, artistic, medical, and marketing domains have all demonstrated substantial interest in comprehending emotions, resulting in the emergence and refinement of techniques and computational methodologies to facilitate automated emotion recognition. In this study, we introduce a novel method named REGL (Recognizing Emotions through Facial Expression and Landmark normalization) aimed at recognizing facial expressions and human emotions depicted in images. REGL comprises a sequential set of steps designed to minimize sample variability, thereby facilitating a finer calibration of the informative aspects that delineate facial patterns. REGL carries out the normalization of facial fiducial points, called landmarks. Through the use of landmark positions, the reliability of the emotion recognition process is significantly improved. REGL also exploits classifiers explicitly tailored for the accurate identification of facial emotions. As related works, the outcomes of our experimentation yielded an average accuracy over 90% by employing Machine Learning algorithms. Differently, we have experimented REGL with varied architectures and datasets including racial factors. We surpass related works considering the following contributions: the REGL method represents an enhanced approach in terms of hit rate and response time, and REGL generates resilient outcomes by demonstrating reduced reliance on both the training set and classifier architecture. Moreover, REGL demonstrated excellent performance in terms of response time enabling low-cost and real-time processing, particularly suitable for devices with limited processing capabilities, such as cellphones. We intend to foster the advancement of robust assistive technologies, facilitate enhancements in computational synthesis techniques, and computational resources.
Palavras-chave:
Multimedia Processing, Affective Computing, Machine Learning-Multimodal Interaction, Facial Patterns, Image Understanding
Referências
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Guilherme Campos, Arthur Zimek, Joerg Sander, Ricardo Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael Houle. 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery 30 (07 2016). DOI: 10.1007/s10618-015-0444-8
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Jeffrey Cohn and Takeo Kanade. 2010. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 94 – 101. DOI: 10.1109/CVPRW.2010.5543262
Dongshun Cui, Guang-Bin Huang, and Tianchi Liu. 2018. ELM based smile detection using Distance Vector. Pattern Recognition 79 (2018), 356–369. DOI: 10.1016/j.patcog.2018.02.019
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Charles Darwin. 2013. The Expression of the Emotions in Man and Animals. Cambridge University Press, England. DOI: 10.1017/CBO9781139833813
Alex Davies and Zoubin Ghahramani. 2014. The Random Forest Kernel and other kernels for big data from random partitions. arXiv:1402.4293 [stat.ML]
Paul Ekman and Wallace V. Friesen. 1971. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology 17, 2 (1971), 124–129. DOI: 10.1037/h0030377
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Gabriel Garrido and Prateek Joshi. 2018. OpenCV 3.X with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality (2nd ed.). Packt Publishing, US.
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Ellen Goeleven, Rudi De Raedt, Lemke Leyman, and Bruno Verschuere. 2008. The Karolinska Directed Emotional Faces: A validation study. Cognition and Emotion 22, 6 (2008), 1094–1118. DOI: 10.1080/02699930701626582
Rafael C Gonzales and Richard E. Woods. 2008. Digital Image Processing (3rd ed.). Pearson, New Jersey, US.
Isabelle Guyon and André Elisseeff. 2003. An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3 (March 2003), 1157–1182.
T. Hassner, E. Harel, S.and Paz, and R. Enbar. 2015. Effective face frontalization in unconstrained images. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Boston, MA, US, 4295–4304. DOI: 10.1109/CVPR.2015.7299058
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The elements of statistical learning: data mining, inference and prediction (2 ed.). Springer, USA. [link]
Jiabei He, Xiaoyu Wen, and Juxiang Zhou. 2023. Advances and Application of Facial Expression and Learning Emotion Recognition in Classroom. In Proceedings of the 2023 6th International Conference on Image and Graphics Processing (Chongqing, China) (ICIGP ’23). Association for Computing Machinery, New York, NY, USA, 23–30. DOI: 10.1145/3582649.3582670
Ursula Hess. 2001. The Communication of Emotion. In Emotions, Qualia and Consciousness. Singapore, 397–409. DOI: 10.1142/9789812810687_0031
Nurulhuda Ismail and Mas Idayu Md. Sabri. 2009. Review of Existing Algorithms for Face Detection and Recognition. In Proceedings of the 8th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (Puerto De La Cruz, Tenerife, Canary Islands, Spain) (CIMMACS’09). World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 30–39.
A. Kumar, K.M. Baalamurugan, and B. Balamurugan. 2022. Real-Time Facial Components Detection Using Haar Classifiers. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, Salem, India, 01–08. DOI: 10.1109/ICAAIC53929.2022.9793034
Uttama Lahiri, Esube Bekele, Elizabeth Dohrmann, Zachary Warren, and Nilanjan Sarkar. 2011. Design of a Virtual Reality Based Adaptive Response Technology for Children with Autism Spectrum Disorder. In Affective Computing and Intelligent Interaction, Sidney D’Mello, Arthur Graesser, Björn Schuller, and Jean-Claude Martin (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 165–174.
Oliver Langner, Ron Dotsch, Gijsbert Bijlstra, Daniel H. J. Wigboldus, Skyler T. Hawk, and Ad van Knippenberg. 2010. Presentation and validation of the Radboud Faces Database. Cognition and Emotion 24, 8 (2010), 1377–1388. DOI: 10.1080/02699930903485076
Oliver Langner, Ron Dotsch, Gijsbert Bijlstra, Daniel H. J. Wigboldus, Skyler T. Hawk, and Ad van Knippenberg. 2010. Presentation and validation of the Radboud Faces Database. Cognition and Emotion 24, 8 (2010), 1377–1388. DOI: 10.1080/02699930903485076
K. Li, F. Xu, J. Wang, Q. Dai, and Y. Liu. 2012. A data-driven approach for facial expression synthesis in video. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Providence, RI, US, 57–64. DOI: 10.1109/CVPR.2012.6247658
Shan Li and Weihong Deng. 2018. Deep Facial Expression Recognition: A Survey. Computing Research Repository (CoRR) abs/1804.08348 (2018). DOI: 10.1109/TAFFC.2020.2981446
Michael Lyons, Miyuki Kamachi, and Jiro Gyoba. 2017. Japanese Female Facial Expression (JAFFE) Database. (7 2017). DOI: 10.6084/m9.figshare.5245003.v2
Dhwani Mehta, Mohammad Faridul Haque Siddiqui, and Ahmad Y. Javaid. 2018. Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors 18, 2 (2018). DOI: 10.3390/s18020416
Karnati Mohan, Ayan Seal, Ondrej Krejcar, and Anis Yazidi. 2021. Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1–12. DOI: 10.1109/TIM.2020.3031835
A Monzo, D; Albiol and M. J. Mossi. 2010. A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors. In 2010 20th International Conference on Pattern Recognition. IEEE, Istanbul, Turkey, 1330–1333. DOI: 10.1109/ICPR.2010.1145
Leandro Persona, Fernando Meloni, and Alessandra Macedo. 2023. An accurate real-time method to detect the smile facial expression. In Anais do XXIX Simpósio Brasileiro de Sistemas Multimídia e Web (Ribeirão Preto/SP). SBC, Porto Alegre, RS, Brasil, 46–55. [link]
Rosalind W. Picard. 2016. Automating the Recognition of Stress and Emotion: From Lab to Real-World Impact. IEEE MultiMedia 23, 3 (July 2016), 3–7. DOI: 10.1109/MMUL.2016.38
I.Michael Revina and W.R. Sam Emmanuel. 2018. A Survey on Human Face Expression Recognition Techniques. Journal of King Saud University - Computer and Information Sciences (2018). DOI: 10.1016/j.jksuci.2018.09.002
Jia S, Wang S, Hu C., Webster PJ, and Li X. 2021. Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods. Front. Psychol. - Sec. Perception Science 11 (15 January 2021), 12p. DOI: 10.3389/fpsyg.2020.580287
F. Z. SALMAN, A. MADANI, and M. KISSI. 2016. Facial Expression Recognition Using Decision Trees. In 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV). IEEE, Beni Mellal, Morocco, 125–130. DOI: 10.1109/CGiV.2016.33
Paul F. Smith, Siva Ganesh, and Ping Liu. 2013. A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of Neuroscience Methods 220, 1 (2013), 85–91. DOI: 10.1016/j.jneumeth.2013.08.024
Rafael Luiz Testa, Cléber Gimenez Corrêa, Ariane Machado-Lima, and Fátima L. S. Nunes. 2019. Synthesis of Facial Expressions in Photographs: Characteristics, Approaches, and Challenges. ACM Comput. Surv. 51, 6, Article 124 (jan 2019), 35 pages. DOI: 10.1145/3292652
Nim Tottenham, James Tanaka, Andrew Leon, Thomas Mccarry, Marcella Nurse, Todd Hare, David Marcus, Alissa Westerlund, Bj Casey, and Charles Nelson. 2009. The NimStim set of Facial Expressions: Judgments from Untrained Research Participants. Psychiatry research 168 (07 2009), 242–9. DOI: 10.1016/j.psychres.2008.05.006
Sana Ullah and Wenhong Tian. 2021. A Systematic Literature Review of Recognition of Compound Facial Expression of Emotions. In Proceedings of the 2020 4th International Conference on Video and Image Processing (Xi’an, China) (ICVIP ’20). Association for Computing Machinery, New York, NY, USA, 116–121. DOI: 10.1145/3447450.3447469
Jean Vaillancourt. 2010. Statistical Methods for Data Mining and Knowledge Discovery. In Proceedings of the 8th International Conference on Formal Concept Analysis (Agadir, Morocco) (ICFCA’10). Springer-Verlag, Berlin, Heidelberg, 51–60.
Paul Viola and Michael J. Jones. 2001. Robust Real-Time Face Detection. International Journal of Computer Vision 57, 2 (2001), 137–154. DOI: 10.1023/B:VISI.0000013087.49260.fb
V. Vonikakis and S. Winkler. 2020. Identity-Invariant Facial Landmark Frontalization For Facial Expression Analysis. In International Conference on Image Processing (ICIP). 2020 IEEE ICIP, Abu Dhabi, United Arab Emirates, 2281–2285. DOI: 10.1109/ICIP40778.2020.9190989
P. Winterle. 2014. Vetores e Geometria Analítica. MAKRON. [link]
Yue Wu and Qiang Ji. 2018. Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision 2 (2018), 115–142. DOI: 10.1007/s11263-018-1097-z
L. Xie, W.; Shen and J. Jiang. 2017. A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis. IEEE Transactions on Multimedia 19, 2 (Feb 2017), 279–292. DOI: 10.1109/TMM.2016.2614429
W. XIE, L. SHEB, M. YANG, and Q. HOU. 2015. Lighting difference based wrinkle mapping for expression synthesis. In 2015 8th International Congress on Image and Signal Processing (CISP). IEEE, Shenyang, China, 636–641. DOI: 10.1109/CISP.2015.7407956
Xiaoming Zhao and Shiqing Zhang. 2016. A Review on Facial Expression Recognition: Feature Extraction and Classification. IETE Technical Re- view 33, 5 (2016), 505–517. DOI: 10.1080/02564602.2015.1117403
Mitra B., Sharma K., Acharya S., Mishra P., and Guglani A. 2022. Real-time Smile Detection using Integrated ML Model. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, Madurai, India, 1374–1381. DOI: 10.1109/ICICCS53718.2022.9788399
Hugo Bohy, Kevin El Haddad, and Thierry Dutoit. 2022. A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter. In 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII). 1–8. DOI: 10.1109/ACII55700.2022.9953896
Guilherme Campos, Arthur Zimek, Joerg Sander, Ricardo Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael Houle. 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery 30 (07 2016). DOI: 10.1007/s10618-015-0444-8
V. Chaugule, D. Abhishek, A. Vijayakumar, P. B. Ramteke, and S. G. Koolagudi. 2016. Product review based on optimized facial expression detection. In 2016 Ninth International Conference on Contemporary Computing (IC3). IEEE, Noida, India, 1–6. DOI: 10.1109/IC3.2016.7880213
Yufang Cheng and Shuhui Ling. 2008. 3D Animated Facial Expression and Autism in Taiwan. In IEEE International Conference on Advanced Learning Technologies (ICALT 2008). IEEE Computer Society, Los Alamitos, CA, USA, 17–19. DOI: 10.1109/ICALT.2008.220
Francois Chollet. 2017. Deep Learning with Python (1st ed.). Manning Publications Co., Greenwich, CT, USA.
Jeffrey Cohn and Takeo Kanade. 2010. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 94 – 101. DOI: 10.1109/CVPRW.2010.5543262
Dongshun Cui, Guang-Bin Huang, and Tianchi Liu. 2018. ELM based smile detection using Distance Vector. Pattern Recognition 79 (2018), 356–369. DOI: 10.1016/j.patcog.2018.02.019
D Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. IEEE, San Diego, CA, USA, 886–893 vol. 1. DOI: 10.1109/CVPR.2005.177
Charles Darwin. 2013. The Expression of the Emotions in Man and Animals. Cambridge University Press, England. DOI: 10.1017/CBO9781139833813
Alex Davies and Zoubin Ghahramani. 2014. The Random Forest Kernel and other kernels for big data from random partitions. arXiv:1402.4293 [stat.ML]
Paul Ekman and Wallace V. Friesen. 1971. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology 17, 2 (1971), 124–129. DOI: 10.1037/h0030377
Hugo. Filho, Oge Marques; Vieira Neto. 1999. Processamento Digital de Imagens. Brasport, Brasil. 30–31 pages.
Gabriel Garrido and Prateek Joshi. 2018. OpenCV 3.X with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality (2nd ed.). Packt Publishing, US.
A. T. Ghorbani, G; Targhi and M. Dehshibi. 2015. HOG and LBP: Towards a robust face recognition system. In 2015 Tenth International Conference on Digital Information Management (ICDIM). IEEE, Jeju, South Korea, 138–141. DOI: 10.1109/ICDIM.2015.7381860
Ellen Goeleven, Rudi De Raedt, Lemke Leyman, and Bruno Verschuere. 2008. The Karolinska Directed Emotional Faces: A validation study. Cognition and Emotion 22, 6 (2008), 1094–1118. DOI: 10.1080/02699930701626582
Rafael C Gonzales and Richard E. Woods. 2008. Digital Image Processing (3rd ed.). Pearson, New Jersey, US.
Isabelle Guyon and André Elisseeff. 2003. An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3 (March 2003), 1157–1182.
T. Hassner, E. Harel, S.and Paz, and R. Enbar. 2015. Effective face frontalization in unconstrained images. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Boston, MA, US, 4295–4304. DOI: 10.1109/CVPR.2015.7299058
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The elements of statistical learning: data mining, inference and prediction (2 ed.). Springer, USA. [link]
Jiabei He, Xiaoyu Wen, and Juxiang Zhou. 2023. Advances and Application of Facial Expression and Learning Emotion Recognition in Classroom. In Proceedings of the 2023 6th International Conference on Image and Graphics Processing (Chongqing, China) (ICIGP ’23). Association for Computing Machinery, New York, NY, USA, 23–30. DOI: 10.1145/3582649.3582670
Ursula Hess. 2001. The Communication of Emotion. In Emotions, Qualia and Consciousness. Singapore, 397–409. DOI: 10.1142/9789812810687_0031
Nurulhuda Ismail and Mas Idayu Md. Sabri. 2009. Review of Existing Algorithms for Face Detection and Recognition. In Proceedings of the 8th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (Puerto De La Cruz, Tenerife, Canary Islands, Spain) (CIMMACS’09). World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 30–39.
A. Kumar, K.M. Baalamurugan, and B. Balamurugan. 2022. Real-Time Facial Components Detection Using Haar Classifiers. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, Salem, India, 01–08. DOI: 10.1109/ICAAIC53929.2022.9793034
Uttama Lahiri, Esube Bekele, Elizabeth Dohrmann, Zachary Warren, and Nilanjan Sarkar. 2011. Design of a Virtual Reality Based Adaptive Response Technology for Children with Autism Spectrum Disorder. In Affective Computing and Intelligent Interaction, Sidney D’Mello, Arthur Graesser, Björn Schuller, and Jean-Claude Martin (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 165–174.
Oliver Langner, Ron Dotsch, Gijsbert Bijlstra, Daniel H. J. Wigboldus, Skyler T. Hawk, and Ad van Knippenberg. 2010. Presentation and validation of the Radboud Faces Database. Cognition and Emotion 24, 8 (2010), 1377–1388. DOI: 10.1080/02699930903485076
Oliver Langner, Ron Dotsch, Gijsbert Bijlstra, Daniel H. J. Wigboldus, Skyler T. Hawk, and Ad van Knippenberg. 2010. Presentation and validation of the Radboud Faces Database. Cognition and Emotion 24, 8 (2010), 1377–1388. DOI: 10.1080/02699930903485076
K. Li, F. Xu, J. Wang, Q. Dai, and Y. Liu. 2012. A data-driven approach for facial expression synthesis in video. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, Providence, RI, US, 57–64. DOI: 10.1109/CVPR.2012.6247658
Shan Li and Weihong Deng. 2018. Deep Facial Expression Recognition: A Survey. Computing Research Repository (CoRR) abs/1804.08348 (2018). DOI: 10.1109/TAFFC.2020.2981446
Michael Lyons, Miyuki Kamachi, and Jiro Gyoba. 2017. Japanese Female Facial Expression (JAFFE) Database. (7 2017). DOI: 10.6084/m9.figshare.5245003.v2
Dhwani Mehta, Mohammad Faridul Haque Siddiqui, and Ahmad Y. Javaid. 2018. Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors 18, 2 (2018). DOI: 10.3390/s18020416
Karnati Mohan, Ayan Seal, Ondrej Krejcar, and Anis Yazidi. 2021. Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1–12. DOI: 10.1109/TIM.2020.3031835
A Monzo, D; Albiol and M. J. Mossi. 2010. A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors. In 2010 20th International Conference on Pattern Recognition. IEEE, Istanbul, Turkey, 1330–1333. DOI: 10.1109/ICPR.2010.1145
Leandro Persona, Fernando Meloni, and Alessandra Macedo. 2023. An accurate real-time method to detect the smile facial expression. In Anais do XXIX Simpósio Brasileiro de Sistemas Multimídia e Web (Ribeirão Preto/SP). SBC, Porto Alegre, RS, Brasil, 46–55. [link]
Rosalind W. Picard. 2016. Automating the Recognition of Stress and Emotion: From Lab to Real-World Impact. IEEE MultiMedia 23, 3 (July 2016), 3–7. DOI: 10.1109/MMUL.2016.38
I.Michael Revina and W.R. Sam Emmanuel. 2018. A Survey on Human Face Expression Recognition Techniques. Journal of King Saud University - Computer and Information Sciences (2018). DOI: 10.1016/j.jksuci.2018.09.002
Jia S, Wang S, Hu C., Webster PJ, and Li X. 2021. Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods. Front. Psychol. - Sec. Perception Science 11 (15 January 2021), 12p. DOI: 10.3389/fpsyg.2020.580287
F. Z. SALMAN, A. MADANI, and M. KISSI. 2016. Facial Expression Recognition Using Decision Trees. In 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV). IEEE, Beni Mellal, Morocco, 125–130. DOI: 10.1109/CGiV.2016.33
Paul F. Smith, Siva Ganesh, and Ping Liu. 2013. A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of Neuroscience Methods 220, 1 (2013), 85–91. DOI: 10.1016/j.jneumeth.2013.08.024
Rafael Luiz Testa, Cléber Gimenez Corrêa, Ariane Machado-Lima, and Fátima L. S. Nunes. 2019. Synthesis of Facial Expressions in Photographs: Characteristics, Approaches, and Challenges. ACM Comput. Surv. 51, 6, Article 124 (jan 2019), 35 pages. DOI: 10.1145/3292652
Nim Tottenham, James Tanaka, Andrew Leon, Thomas Mccarry, Marcella Nurse, Todd Hare, David Marcus, Alissa Westerlund, Bj Casey, and Charles Nelson. 2009. The NimStim set of Facial Expressions: Judgments from Untrained Research Participants. Psychiatry research 168 (07 2009), 242–9. DOI: 10.1016/j.psychres.2008.05.006
Sana Ullah and Wenhong Tian. 2021. A Systematic Literature Review of Recognition of Compound Facial Expression of Emotions. In Proceedings of the 2020 4th International Conference on Video and Image Processing (Xi’an, China) (ICVIP ’20). Association for Computing Machinery, New York, NY, USA, 116–121. DOI: 10.1145/3447450.3447469
Jean Vaillancourt. 2010. Statistical Methods for Data Mining and Knowledge Discovery. In Proceedings of the 8th International Conference on Formal Concept Analysis (Agadir, Morocco) (ICFCA’10). Springer-Verlag, Berlin, Heidelberg, 51–60.
Paul Viola and Michael J. Jones. 2001. Robust Real-Time Face Detection. International Journal of Computer Vision 57, 2 (2001), 137–154. DOI: 10.1023/B:VISI.0000013087.49260.fb
V. Vonikakis and S. Winkler. 2020. Identity-Invariant Facial Landmark Frontalization For Facial Expression Analysis. In International Conference on Image Processing (ICIP). 2020 IEEE ICIP, Abu Dhabi, United Arab Emirates, 2281–2285. DOI: 10.1109/ICIP40778.2020.9190989
P. Winterle. 2014. Vetores e Geometria Analítica. MAKRON. [link]
Yue Wu and Qiang Ji. 2018. Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision 2 (2018), 115–142. DOI: 10.1007/s11263-018-1097-z
L. Xie, W.; Shen and J. Jiang. 2017. A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis. IEEE Transactions on Multimedia 19, 2 (Feb 2017), 279–292. DOI: 10.1109/TMM.2016.2614429
W. XIE, L. SHEB, M. YANG, and Q. HOU. 2015. Lighting difference based wrinkle mapping for expression synthesis. In 2015 8th International Congress on Image and Signal Processing (CISP). IEEE, Shenyang, China, 636–641. DOI: 10.1109/CISP.2015.7407956
Xiaoming Zhao and Shiqing Zhang. 2016. A Review on Facial Expression Recognition: Feature Extraction and Classification. IETE Technical Re- view 33, 5 (2016), 505–517. DOI: 10.1080/02564602.2015.1117403
Publicado
14/10/2024
Como Citar
MACEDO, Alessandra Alaniz; PERSONA, Leandro; MELONI, Fernando.
Recognition of Emotions through Facial Geometry with Normalized Landmarks. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 30. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 257-266.
DOI: https://doi.org/10.5753/webmedia.2024.243252.
