SciELO - Scientific Electronic Library Online

 
vol.19 issue3HW/SW Co-Design of a Specific Accelerator for Robotic Computer VisionPredicting Software Product Quality: A Systematic Mapping Study author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.19 n.3 México Jul./Sep. 2015

 

Artículos

 

Facial Geometry Identification through Fuzzy Patterns with RGBD Sensor

 

Víctor Fernández-Cervantes1, Arturo García1, Marco Antonio Ramos2, Andrés Méndez1

 

1 Instituto Politécnico Nacional, CINVESTAV, Guadalajara, México. vfernand@gdl.cinvestav.mx, aggarcia@gdl.cinvestav.mx, amendez@gdl.cinvestav.mx

2 Universidad Autónoma del Estado de México, México. mramos@univ-tlse1.fr

Corresponding author is Víctor Fernández Cervantes.

 

Article received on 24/11/2014.
Accepted on 17/04/2015.

 

Abstract

Automatic human facial recognition is an important and complicated task; it is necessary to design algorithms capable of recognizing the constant patterns in the face and to use computing resources efficiently. In this paper we present a novel algorithm to recognize the human face in real time; the system's input is the depth and color data from the Microsoft KinectTM device. The algorithm recognizes patterns/shapes on the point cloud topography. The template of the face is based in facial geometry; the forensic theory classifies the human face with respect to constant patterns: cephalometric points, lines, and areas of the face. The topography, relative position, and symmetry are directly related to the craniometric points. The similarity between a point cloud cluster and a pattern description is measured by a fuzzy pattern theory algorithm. The face identification is composed by two phases: the first phase calculates the face pattern hypothesis of the facial points, configures each point shape, the related location in the areas, and lines of the face. Then, in the second phase, the algorithm performs a search on these face point configurations.

Keywords: Kinect, RGBD, fuzzy logic, face detection, face segmentation.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Acknowledgements

This work has been funded by CONACYT scholarship number 212753. The authors want to thank Víctor Rodríguez, Isela Ayar, Juan Manuel Rodríguez, Emily "Godzilla" Marlen Rodríguez, Stuart and Maria Embleton who participated in the project.

 

References

1. Inaba, R., Watanabe, E., & Kashiko, K. (2003). Security applications of optical face recognition system: access control in e-Learning. Departlnent of Mathematical and Physical Sciences, Japan Woinen's University, pp. 255-261.         [ Links ]

2. Belhumeur, P.N., Hespanha, J.P., & Kriegman, D.J. (1997). Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, pp. 711-720, doi: 10.1109/34.598228.         [ Links ]

3. Blanz, V. & Vetter,T. (2003). Face recognition based on fitting a 3d morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1063-1074, doi: 10.1109/TPAMI.2003.1227983.         [ Links ]

4. Kare, S., Samal, A., & Marx, D.D. (2008). Using bidimensional regression to assess face similarity. Machine Vision and Applications, Vol. 21, No. 3, pp. 261-274, doi: 10.1007/s00138-008-0158-7.         [ Links ]

5. Singh, R., Vatsa, M., & Noore, A. (2009). Face recognition with disguise and single gallery images. Image and Vision Computing, Vol. 27, No. 3, pp. 245-257, doi: 10.1016/j.imavis.2007.06.010.         [ Links ]

6. Kresimir, D. & Mislav, G. (2007). Face Recognition. I-Tech Education.         [ Links ]

7. Ponce, J. & Karahoca, A. (2009). State of the Art in Face Recognition. I-Tech Education.         [ Links ]

8. Xie, S., Shan, S., Chen, X., & Gao, W. (2008). V-LGBP: Volume based Local Gabor Binary Patterns for face representation and recognition. 19th International Conference on Pattern Recognition (ICPR), pp. 1-4, doi: 10.1109/ICPR.2008.4761374.         [ Links ]

9. Zou, J., Ji, Q., & Nagy, G. (2007). A comparative study of local matching approach for face recognition. IEEE Trans. on Image Proc, pp. 2617-2628, doi: 10.1109/TIP.2007.904421.         [ Links ]

10. Breitenstein, M.D., Kuettel, D., Weise, T., Van Gool, L., & Pfister, H. (2008). Real-time face pose estimation from single range images. IEEE Conference on Computer Vision and Pattern Recognition, doi: 10.1109/CVPR.2008.4587807.         [ Links ]

11. Seemann, E., Nickel, K., & Stiefelhagen, R. (2004). Head pose estimation using stereo vision for human-robot interaction. Proc. 6th Int. Conf. AFGR, Seoul, Korea, doi: 10.1109/AFGR.2004.1301603.         [ Links ]

12. Valenti, R., Sebe, N., & Gevers, T. (2008). Simple and efficient visual gaze estimation. Workshop on Multimodal Interactions Analysis of Users in a Controlled Environment.         [ Links ]

13. Makinen, E. & Raisamo R., (2008). Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 30, No. 3, doi: 10.1109/ICSPC.2007.4728416.         [ Links ]

14. Yang, Z., Li, M., & Ai, H. (2006). An Experimental Study on Automatic Face Gender Classification. Proc. 18th IEEE Int'l Conf. Pattern Recognition, Vol. 3, pp. 1099-1102, doi: 10.1109/ICPR.2006.247.         [ Links ]

15. Ravi, S. & Wilson, S. (2010). Face Detection with Facial Features and Gender Classification Based On Support Vector Machine. International Journal of Imaging Science and Engineering.

16. Yang, M.H., Kriegman, J., & Ahuja, N. (2002). Detecting Faces in Images: A Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, doi: 10.1109/34.982883.         [ Links ]

17. Zadeh, L.A. (2010). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, No. 1, pp. 28-44, doi: 10.1109/TSMC.1973.5408575.         [ Links ]

18. Yang, M.H., Kriegman, D. & Ahuja, N. (2002). Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 34-58, doi: 10.1109/34.982883.         [ Links ]

19. Hjelmas, E. (2001). Face Detection: A Survey. Computer Vision and Image Understanding, Vol. 83, pp. 236-274, doi: 10.1006/cviu.2001.0921.         [ Links ]

20. Nasrabadi, A. & Haddadnia, J. (2010). Face detection base on fuzzy skin region segmentation. Education Technology and Computer (ICETC), 2nd International Conference, Vol. 5, pp. 22-24, doi: 10.1109/ICETC.2010.5530048.         [ Links ]

21. Robert, M. & George, P. (2007). Facial Geometry, Graphic Facial Analisis for Forensic Artist. Charles Thomas Publisher.         [ Links ]

22. Grenander, U. (1996). Elements of Pattern Theory. Baltimore: Johns Hopkins University Press.         [ Links ]

23. Smisek, J., Jancosek, M., & Pajdla, T. (2011). 3D with Kinect. IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1154-1160, doi: 10.1007/978-1-4471-4640-7_1.         [ Links ]

24. Burrus, N. (Online) RGBDemo Ver. 0.5, Available Kinect calibration. Available: http://burrus.name/index.php/Research/KinectCalibration        [ Links ]

25. Freedman, B., Shpunt, A., Machline, M. & Arieli, Y. (2012). Depth mapping using projected patterns. US Patente 8,150,142,B2.         [ Links ]

26. Hartley, R. & Zisserman, A. (2003). Multiple View Geometry in Computer Vision. 2 ed., Cambridge.         [ Links ]

27. Bowyer, K., Chang, K., & Flynn, P. (2006). A survey of pproaches and challenges in 3D and multi-modal 3D+2D face recognition. Computer Vision and Image Understanding, Vol. 101, No. 1, pp. 1-15, doi: 10.1016/j.cviu.2005.05.005.         [ Links ]

28. Samir, C., Srivastava, A., & Daoudi, M. (2006). Threedimensional face recognition using shapes of facial curves. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, doi: 10.1109/TPAMI.2006.235.         [ Links ]

29. Gokberk, B., Dutagaci, H., Ulas, A., Akarun, L., & Sankur, B. (2008). Representation plurality and fusion for 3D face recognition. IEEE Trans. on Systems, Man, and Cybernetics, Vol. 38, No, 1, pp. 155-173, doi: 10.1109/TSMCB.2007.908865.         [ Links ]

30. Llonch, R.S., Kokiopoulou, E., Tosic, I., & Frossard, I. (2008). 3D face recognition using sparse spherical representations. 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1-4, doi: 10.1109/ICPR.2008.4761682.         [ Links ]

31. Bevilacqua, V., Casorio, P., & Mastronardi, G. (2008). Extending hough transform to a points cloud for 3D-Face Nose-Tip detection. 4th International Conference on Intelligent Computing (ICIC 2008), Shanghai, China, pp. 15-18, doi: 10.1007/978-3-540-85984-0_144.         [ Links ]

32. OpenNI (Online). OpenNI. Available: http://www.openni.org/.         [ Links ]

33. Microsoft (Online). Microsoft Kinect SDK. Available: http://www.microsoft.com/en-us/kinectforwindows/.         [ Links ]

34. OpenKinect (Online). OpenKinect. Available: https://github.com/OpenKinect/libfreenect/.         [ Links ]

35. Smisek, J., Jancosek, M., & Pajdla, T. (2011). 3D with Kinect. IEEE ICCV Workshops, pp. 1154-1160.         [ Links ]

36. Stoyanov, T., Louloudi, A., Andreasson, H., & Lilienthal, A. (2011). Comparative evaluation of range sensor accuracy in indoor environments. Eur. Conf. Mobile Robots, pp. 19-24.         [ Links ]

37. Khoshelham, K. & Elberink, S. (2012). Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors, Vol. 12, No. 2, pp. 437-1454, doi: 10.3390/s120201437.         [ Links ]

38. Dutta, T. (2012). Evaluation of the Kinect sensor for 3-D kinematic measurement in the workplace. Appl. Ergonom, Vol. 43, No. 4, pp. 645-649, doi: 10.1016/j.apergo.2011.09.011.         [ Links ]

39. Obdrzalek, S., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., & Pavel, M. (2012). Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. IEEE EMBC, pp. 1188-1193, doi: 10.1109/EMBC.2012.6346149.         [ Links ]

40. Comaniciu, D. & Meer, P. (2002). Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 603-619, doi: 10.1109/34.1000236.         [ Links ]

41. Jenke, P., Wand, M., Bokeloh, M., Schilling, A., & Strafter, W. (2006). Bayesian Point Cloud Reconstruction. Computer Graphics Forum, Vol. 25, No. 3, pp. 379-388, doi: 10.1111/j.1467-8659.2006.00957.x.         [ Links ]

42. Meers, S. & Ward, K. (2009). Face Recognition Using a Time-of-Flight Camera. Computer Graphics, Imaging and Visualization, pp. 377-382, doi: 10.1109/CGIV.2009.44.         [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License