A Statistical Quadtree Decomposition to Improve Face Analysis

Vagner Amaral, Gilson A. Giraldi, Carlos E. Thomaz

2016

Abstract

The feature extraction is one of the most important steps in face analysis applications and this subject always received attention in the computer vision and pattern recognition areas due to its applicability and wide scope. However, to define the correct spatial relevance of physiognomical features remains a great challenge. It has been proposed recently, with promising results, a statistical spatial mapping technique that highlights the most discriminating facial features using some task driven information from data mining. Such priori information has been employed as a spatial weighted map on Local Binary Pattern (LBP), that uses Chi-Square distance as a nearest neighbour based classifier. Intending to reduce the dimensionality of LBP descriptors and improve the classification rates we propose and implement in this paper two quad-tree image decomposition algorithms to task related spatial map segmentation. The first relies only on split step (top-down) of distinct regions and the second performs the split step followed by a merge step (bottom-up) to combine similar adjacent regions. We carried out the experiments with two distinct face databases and our preliminary results show that the top-down approach achieved similar classification results to standard segmentation using though less regions.

References

  1. Ahonen, T., Hadid, A., and Pietikäinen, M. (2006). Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Analisys and Machine Intelligence, 28:2037-2041.
  2. Amaral, V., Giraldi, G. A., and Thomaz, C. E. (2013). LBP estatístico aplicado ao reconhecimento de express o˜es faciais. In Proceedings of the X Encontro Nacional de Inteligeˆncia Artificial e Computacional, ENIAC'13, Fortaleza, Ceara, Brasil.
  3. Amaral, V., Giraldi, G. A., and Thomaz, C. E. (2014). Segmentac¸a˜o espacial na˜o uniforme aplicada ao reconhecimento de geˆnero e expressoes faciais. In Proceedings of the XI Encontro Nacional de Inteligeˆncia Artificial e Computacional, ENIAC'14, Sa˜o Carlos, Sa˜o Paulo, Brazil.
  4. Amaral, V., Giraldi, G. A., and Thomaz, C. E. (2015). Statistical and cognitive spatial mapping applied to face analysis. In Proceedings of the 28th SIBGRAPI, Conference on Graphics, Patterns and Images - Workshop of Works in Progress, Salvador, Bahia, Brazil.
  5. Amaral, V. and Thomaz, C. E. (2013). Um estudo sobre o detalhamento espacial de descritores locais aplicados ao reconhecimento de geˆnero e expresso˜es faciais. In Anais do 3 Simpósio de Pesquisa do Grande ABC, Sa˜o Bernardo do Campo, Sa˜o Paulo, Brazil.
  6. Blais, C., Roy, C., Fiset, D., Arguin, M., and Gosselin, F. (2012). The eyes are not the window to basic emotions. Neuropsychologia, 50(12):2830-2838.
  7. Conde-Marquez, G. R., Escalante, H. J., and Sucar, E. (2011). Simplified quadtree image segmentation for image annotation. In Sucar, E. and Escalante, H. J., editors, Proceedings of the 2010 Automatic Image Annotation and Retrieval Workshop (2010), volume 719, pages 24-34. CEUR-Workshop Proceedings.
  8. Fu, G., Zhao, H., Li, C., and Shi, L. (2013). Segmentation for high-resolution optical remote sensing imagery using improved quadtree and region adjacency graph technique. Remote Sensing, 5(7):3259.
  9. Muhsin, Z. F., Rehman, A., Altameem, A., Saba, T., and Uddin, M. (2014). Improved quadtree image segmentation approach to region information. The Imaging Science Journal, 62(1):56-62.
  10. Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51-59.
  11. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(7):971-987.
  12. Phillips, P. J., Moon, H., Rizvi, S. A., and Rauss, P. (2000). The FERET evaluation methodology for face-recognition algorithms. In IEEE Transaction on Pattern Analysis and Machine Intelligence, volume 22, pages 1090-1104, Washington, DC, USA. IEEE Computer Society.
  13. Pietikäinen, M., Zhao, G., Hadid, A., and Ahonen, T. (2011). Computer Vision Using Local Binary Patterns. Number 40 in Computational Imaging and Vision. Springer.
  14. Samet, H. (1984). The quadtree and related hierarchical data structures. ACM Comput. Surv., 16(2):187-260.
  15. Santarcangelo, V., Farinella, G. M., and Battiato, S. (2015). Gender recognition: Methods, datasets and results. In IEEE International Conference on Multimedia Expo Workshops (ICMEW 2015), pages 1-6.
  16. Scholefield, A. and Dragotti, P. L. (2014). Quadtree structured image approximation for denoising and interpolation. IEEE Transactions on IMage Processing, 23:1226-1239.
  17. Shan, C. (2012). Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters, 33(4):431-437.
  18. Shan, C., Gong, S., and McOwan, P. W. (2005). Robust facial expression recognition using local binary patterns. In ICIP 2005. IEEE International Conference on Image Processing, 2005. IEEE.
  19. Shan, C., Gong, S., and McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27(6):803-816.
  20. Thomaz, C. E. and Giraldi, G. A. (2010). A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing, 28:902-913.
  21. Torrisi, A., Farinella, G. M., Puglisi, G., and Battiato, S. (2015). Selecting discriminative clbp patterns for age estimation. In IEEE International Conference on Multimedia Expo Workshops (ICMEW), pages 1-6.
  22. Usó, A. M. (2003). A quadtree-based unsupervised segmentation algorithm for fruit visual inspection. In López, F. J. P., Campilho, A. C., de la Blanca, N. P., and Sanfeliu, A., editors, IbPRIA, volume 2652 of Lecture Notes in Computer Science, pages 510-517. Springer.
  23. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. Acm Computing Surveys, 35(4):399-458.
Download


Paper Citation


in Harvard Style

Amaral V., Giraldi G. and Thomaz C. (2016). A Statistical Quadtree Decomposition to Improve Face Analysis . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 375-380. DOI: 10.5220/0005823903750380


in Bibtex Style

@conference{icpram16,
author={Vagner Amaral and Gilson A. Giraldi and Carlos E. Thomaz},
title={A Statistical Quadtree Decomposition to Improve Face Analysis},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={375-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005823903750380},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Statistical Quadtree Decomposition to Improve Face Analysis
SN - 978-989-758-173-1
AU - Amaral V.
AU - Giraldi G.
AU - Thomaz C.
PY - 2016
SP - 375
EP - 380
DO - 10.5220/0005823903750380