Fatiamento em Reconhecimento Facial: Uma Investigação Baseada em Unidades de Ação para possíveis melhorias através da seleção de fatias da face em Nuvens de Pontos 3D
Resumo
Neste artigo e proposta uma investigação para fatiamento de nuvens de pontos 3D, que representam rostos, dentro do problema de reconhecimento de faces. Para isso, e feita uma comparação entre os resultados de classificação com e sem fatiamento das faces, utilizando características geométricas no processo de extração de informação. Também foram comparados dois tipos de segmentação em subnuvens, o fatiamento Triaxial e o fatiamento em Pizza com Superposição - aqui proposto. O objetivo das comparações é investigar se a seleção de regiões específicas da face 3D pode aprimorar os resultados da classificação. Os resultados mostram-se promissores, indicando que um refinamento da técnica pode gerar uma classificação de indivíduos robusta a variações da face.
Palavras-chave:
Reconhecimento de faces, fatiamento, 3D, nuvens de pontos, FACS
Referências
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Ekman, P. (1980). Asymmetry in facial expression. Science, 209:833–834.
Ekman, P. and Friesen, W. V. (1976). Measuring facial movement. Environmental psychology and nonverbal behavior, 1(1):56–75.
Ekman, P. and Friesen, W. V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Moviment. Consulting Psychologists Press, Palo Alto.
Erdogmus, N. and Marcel, S. (2013). Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 1–6.
Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381–395.
G. Gordon, G. (1991). Face recognition based on depth maps and surface curvature. SPIE Proc. Geometric Methods Comput. Vision, 1570:234–247.
Hackel, T., Wegner, J. D., and Schindler, K. (2016). Contour detection in unstructured 3d point clouds. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1610–1618.
Hager, J. C. and Ekman, P. (2005). The asymmetry of facial actions is inconsistent with models of hemispheric specialization. In What the Face RevealsBasic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS), pages 40–58. Oxford University Press.
Hariri, W., Tabia, H., Farah, N., Benouareth, A., and Declercq, D. (2016). 3d face recognition using covariance based descriptors. Pattern Recognition Letters, 78:1–7.
Jafri, R. and Arabnia, H. (2009). A survey of face recognition techniques. JIPS, 5:41–68.
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Lee, J. and Milios, E. (1991). Matching range images of human faces. In [1990] Proceedings Third International Conference on Computer Vision, pages 722 – 726.
Lin, C.-H., Chen, J.-Y., Su, P.-L., and Chen, C.-H. (2014). Eigen-feature analysis of weighted covariance matrices for lidar point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 94:70 – 79.
Makioka, T., Kuriyaki, Y., Uchimura, K., and Satonaka, T. (2016). Quantitative study of facial expression asymmetry using objective measure based on neural networks. In 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pages 1–4.
Moreno, A. and Sánchez, A. (2004). Gavabdb: a 3d face database.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Jin Chang, Hoffman, K., Marques, J., Jaesik Min, and Worek, W. (2005). Overview of the face recognition grand challenge. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 947–954 vol. 1.
Reddy, A. M., Kishore, M. R., Sreenivasulu, P., and Jyothi, V. (2018). A survey of face recognition system. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), Volume 5.
Rusu, R. B. and Cousins, S. (2011). 3d is here: Point cloud library (pcl. In In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1–4. IEEE.
Sackeim, H., Gur, R., and Saucy, M. (1978). Emotions are expressed more intensely on the left side of the face. Science, 202(4366):434–436.
Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., and Akarun, L. (2008). Bosphorus database for 3d face analysis. In Schouten, B., Juul, N. C., Drygajlo, A., and Tistarelli, M., editors, Biometrics and Identity Management, pages 47–56, Berlin, Heidelberg. Springer Berlin Heidelberg.
Shepley, A. J. (2019). Face recognition in unconstrained conditions: A systematic review.
Siqueira, R., Alexandre, G. R., Soares, J. M., and Thé, G. A. P. (2018). Triaxial slicing for 3-d face recognition from adapted rotational invariants spatial moments and minimal keypoints dependence. IEEE Robotics and Automation Letters, 3(4):3513–3520.
Soltanpour, S., Boufama, B., and Wu, Q. M. J. (2017). A survey of local feature methods for 3d face recognition. Pattern Recognition, 72.
Tanaka, H. T., Ikeda, M., and Chiaki, H. (1998). Curvature-based face surface recognition using spherical correlation. principal directions for curved object recognition. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 372–377.
Yin, L., Wei, X., Sun, Y., Wang, J., and Rosato, M. J. (2006). A 3d facial expression database for facial behavior research. In Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, FGR ’06, pages 211–216, Washington, DC, USA. IEEE Computer Society.
Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Comput. Surv., 35(4):399–458.
Bowyer, K. W., Chang, K., and Flynn, P. (2004). A survey of approaches to threedimensional face recognition. In null, pages 358–361. IEEE.
Ekman, P. (1980). Asymmetry in facial expression. Science, 209:833–834.
Ekman, P. and Friesen, W. V. (1976). Measuring facial movement. Environmental psychology and nonverbal behavior, 1(1):56–75.
Ekman, P. and Friesen, W. V. (1978). Facial Action Coding System: A Technique for the Measurement of Facial Moviment. Consulting Psychologists Press, Palo Alto.
Erdogmus, N. and Marcel, S. (2013). Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 1–6.
Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381–395.
G. Gordon, G. (1991). Face recognition based on depth maps and surface curvature. SPIE Proc. Geometric Methods Comput. Vision, 1570:234–247.
Hackel, T., Wegner, J. D., and Schindler, K. (2016). Contour detection in unstructured 3d point clouds. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1610–1618.
Hager, J. C. and Ekman, P. (2005). The asymmetry of facial actions is inconsistent with models of hemispheric specialization. In What the Face RevealsBasic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS), pages 40–58. Oxford University Press.
Hariri, W., Tabia, H., Farah, N., Benouareth, A., and Declercq, D. (2016). 3d face recognition using covariance based descriptors. Pattern Recognition Letters, 78:1–7.
Jafri, R. and Arabnia, H. (2009). A survey of face recognition techniques. JIPS, 5:41–68.
Kavita, M. M. K. (2016). A survey of face recognition techniques. International Journal of Scientific and Research Publications (IJSRP), Volume 6.
Lee, J. and Milios, E. (1991). Matching range images of human faces. In [1990] Proceedings Third International Conference on Computer Vision, pages 722 – 726.
Lin, C.-H., Chen, J.-Y., Su, P.-L., and Chen, C.-H. (2014). Eigen-feature analysis of weighted covariance matrices for lidar point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 94:70 – 79.
Makioka, T., Kuriyaki, Y., Uchimura, K., and Satonaka, T. (2016). Quantitative study of facial expression asymmetry using objective measure based on neural networks. In 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pages 1–4.
Moreno, A. and Sánchez, A. (2004). Gavabdb: a 3d face database.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Jin Chang, Hoffman, K., Marques, J., Jaesik Min, and Worek, W. (2005). Overview of the face recognition grand challenge. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 947–954 vol. 1.
Reddy, A. M., Kishore, M. R., Sreenivasulu, P., and Jyothi, V. (2018). A survey of face recognition system. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), Volume 5.
Rusu, R. B. and Cousins, S. (2011). 3d is here: Point cloud library (pcl. In In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1–4. IEEE.
Sackeim, H., Gur, R., and Saucy, M. (1978). Emotions are expressed more intensely on the left side of the face. Science, 202(4366):434–436.
Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., and Akarun, L. (2008). Bosphorus database for 3d face analysis. In Schouten, B., Juul, N. C., Drygajlo, A., and Tistarelli, M., editors, Biometrics and Identity Management, pages 47–56, Berlin, Heidelberg. Springer Berlin Heidelberg.
Shepley, A. J. (2019). Face recognition in unconstrained conditions: A systematic review.
Siqueira, R., Alexandre, G. R., Soares, J. M., and Thé, G. A. P. (2018). Triaxial slicing for 3-d face recognition from adapted rotational invariants spatial moments and minimal keypoints dependence. IEEE Robotics and Automation Letters, 3(4):3513–3520.
Soltanpour, S., Boufama, B., and Wu, Q. M. J. (2017). A survey of local feature methods for 3d face recognition. Pattern Recognition, 72.
Tanaka, H. T., Ikeda, M., and Chiaki, H. (1998). Curvature-based face surface recognition using spherical correlation. principal directions for curved object recognition. In Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 372–377.
Yin, L., Wei, X., Sun, Y., Wang, J., and Rosato, M. J. (2006). A 3d facial expression database for facial behavior research. In Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, FGR ’06, pages 211–216, Washington, DC, USA. IEEE Computer Society.
Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Comput. Surv., 35(4):399–458.
Publicado
15/10/2019
Como Citar
MARTINS, Patricia; SOARES, José; THÉ, George.
Fatiamento em Reconhecimento Facial: Uma Investigação Baseada em Unidades de Ação para possíveis melhorias através da seleção de fatias da face em Nuvens de Pontos 3D. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 926-937.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9346.
