Eye localization for face recognition

Paola Campadelli; Raffaella Lanzarotti; Giuseppe Lipori

RAIRO - Theoretical Informatics and Applications (2006)

  • Volume: 40, Issue: 2, page 123-139
  • ISSN: 0988-3754

Abstract

top
We present a novel eye localization method which can be used in face recognition applications. It is based on two SVM classifiers which localize the eyes at different resolution levels exploiting the Haar wavelet representation of the images. We present an extensive analysis of its performance on images of very different public databases, showing very good results.

How to cite

top

Campadelli, Paola, Lanzarotti, Raffaella, and Lipori, Giuseppe. "Eye localization for face recognition." RAIRO - Theoretical Informatics and Applications 40.2 (2006): 123-139. <http://eudml.org/doc/249624>.

@article{Campadelli2006,
abstract = { We present a novel eye localization method which can be used in face recognition applications. It is based on two SVM classifiers which localize the eyes at different resolution levels exploiting the Haar wavelet representation of the images. We present an extensive analysis of its performance on images of very different public databases, showing very good results. },
author = {Campadelli, Paola, Lanzarotti, Raffaella, Lipori, Giuseppe},
journal = {RAIRO - Theoretical Informatics and Applications},
keywords = {Eye localization; face recognition; Haar wavelets; support vector machines.; eye localization; support vector machines},
language = {eng},
month = {7},
number = {2},
pages = {123-139},
publisher = {EDP Sciences},
title = {Eye localization for face recognition},
url = {http://eudml.org/doc/249624},
volume = {40},
year = {2006},
}

TY - JOUR
AU - Campadelli, Paola
AU - Lanzarotti, Raffaella
AU - Lipori, Giuseppe
TI - Eye localization for face recognition
JO - RAIRO - Theoretical Informatics and Applications
DA - 2006/7//
PB - EDP Sciences
VL - 40
IS - 2
SP - 123
EP - 139
AB - We present a novel eye localization method which can be used in face recognition applications. It is based on two SVM classifiers which localize the eyes at different resolution levels exploiting the Haar wavelet representation of the images. We present an extensive analysis of its performance on images of very different public databases, showing very good results.
LA - eng
KW - Eye localization; face recognition; Haar wavelets; support vector machines.; eye localization; support vector machines
UR - http://eudml.org/doc/249624
ER -

References

top
  1. O. Arandjelovic, G. Shakhnarovich, J. Fisher, R. Cipolla and T. Darrell, Face recognition with image sets using manifold density divergence, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05) (2005).  
  2. S. Arca, P. Campadelli and R. Lanzarotti, A face recognition system based on automatically determined facial fiducial points. Pattern Recognition (2006) 432–443.  
  3. J.R. Beveridge, D. Bolme, B.A. Draper and M. Teixeira, The CSU face identification evaluation system. its purpose, features, and structure. Machine vision and applications16 (2005) 128–138.  
  4. P. Campadelli and R. Lanzarotti, Fiducial point localization in color images of face foregrounds. Image Vision Comput. J.22 (2004) 863–872.  
  5. P. Campadelli, R. Lanzarotti and G. Lipori, Face localization in color images with complex background, in Proceedings of the IEEE International Workshop on Computer Architecture for Machine Perception (CAMP 2005), Palermo, Italy (2005) 243–248.  
  6. H. Cevikalp, M. Neamtu, M. Wilkes and A. Barkaba, Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Machine Intell.27 (2005) 4–13.  
  7. K.I. Chang, K.W. Bowyer and P.J. Flynn, An evaluation of multimodal 2d+3d face biometrics. IEEE Trans. Pattern Anal. Machine Intell.27 (2005) 619–624.  
  8. C. Liu, Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Machine Intell.26 (2004) 572–581.  
  9. N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines. Cambridge University Press (2000).  
  10. D. Cristinacce and T.F. Cootes, A comparison of shape constrained facial feature detectors. Proceedings International conference on Automatic Face and Gesture Recognition (2004).  
  11. The BANCA Database. .  URIhttp://www.ee.surrey.ac.uk/Research/VSSP/banca/
  12. The BioID Database. .  URIhttp://www.humanscan.de/support/downloads/facedb.php
  13. The Face Recogniton Grand Challenge Database. .  URIhttp://www.frvt.org/FRGC/
  14. The FERET Database. .  URIhttp://www.itl.nist.gov/iad/humanid/feret/
  15. The XM2VTS Database. .  URIhttp://www.ee.surrey.ac.uk/Research/VSSP/xm2vtsdb/
  16. G. Givens, J.R. Beveridge, B.A. Draper, P. Grother and P.J. Phillips, How features of the human face affect recognition: a statistical comparison of three face recognition algorithms, in Proceedings of IEEE on Computer Vision and Pattern Recognition (2004).  
  17. Y. Gizatdinova and V. Surakka, Feature-based detection of facial landmarks from neutral and expressive facial images. IEEE Trans. Pattern Anal. Machine Intell.28 (2006) 135–139.  
  18. V. Bruce, F. Fogelman Soulie, H. Wechsler, P.J. Phillips and T. Huang Eds. Face Recognition: from theory to applications. Springer-Verlag (1998).  
  19. M. Hamouz, J. Kittler, J.K. Kamarainen, P. Paalanen, H. Kälviäinen and J. Matas. Feature-based affine invariant localization of faces. IEEE Trans. Pattern Anal. Machine Intell.27 (2005) 1490–1495.  
  20. INRIA. .  URIhttp://www-prima.inrialpes.fr/FGnet/data/07-XM2VTS/
  21. O. Jesorsky, K.J. Kirchberg and R.W. Frischholz, Robust face detection using the hausdorff distance. Lect. Notes Comput. Sci.2091 (2001) 212–227.  
  22. Q. Ji, H. Wechsler, A. Duchowski and M. Flickner, Special issue: eye detection and tracking. Comput. Vision Image Understanding98 (2005) 1–3.  
  23. T. Joachims, SVMlight package implementation. .  URIhttp://svmlight.joachims.org/
  24. J. Kim, J. Choi, J. Yi and M. Turk, Effective representation using ica for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Machine Intell.27 (2005) 1977–1981.  
  25. Y. Ma, X. Ding, Z. Wang and N. Wang, Robust precise eye location under probabilistic framework, in Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR 04) (2004).  
  26. B. Moghaddam, C. Nastar and A. Pentland, A bayesian similarity measure for direct image matching, in International Conference on Pattern Recognition, Vienna, Austria (1996).  
  27. K. Okada, J. Steffens, T. Maurer, H. Hong E. Elagin, H. Neven and C. von der Malsburg, The bochum/usc face recognition system and how it fared in the feret phase iii test. In H. Wechsler and Huang [18] 186–205.  
  28. M. Osadchy, M.L. Miller and Y. LeCun, Synergistic face detection and pose estimation with energy-based models (2005) 1017–1024.  
  29. C.P. Papageorgiou and T. Poggio, Trainable pedestrian detection, in Proceedings of International Conference on Image Processing (1999) 35–39.  
  30. J. Phillips, H. Moon, S.A. Rizvi and P.J. Rauss, The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Machine Intell.22.  
  31. S. Romdhani and T. Vetter, Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05) (2005).  
  32. H. Schneiderman and T. Kanade, Object detection using the statistic of parts. Int. J. Comput. Vision56 (2004) 151–177.  
  33. S. Shan, Y. Chang, W. Gao and B. Cao, Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution. International Conference on Automatic Face and Gesture Recognition (FG04) (2004) 314–320.  
  34. X. Tang, Z. Ou, T. Su, H. Sun and P. Zhao, Robust precise eye location by adaboost and svm techniques, in Proceedings of the International Symposium on Neural Networks (2005) 93–98.  
  35. M. Turker and A. Pentland, Face recognition using eigenfaces. J. Cognitive Neuroscience3 (1991).  
  36. Vapnik, The nature of statistical learning theory. Springer (1995).  
  37. P. Viola and M. Jones, Robust real time object detection. Inter. J. Comput. Vision57 (2004) 137–154.  
  38. P. Wang, M. Green, Q. Ji and J. Wayman, Automatic eye detection and its validation, in Proceedings of the Workshop FRGC in the IEEE conference on Computer Vision and Pattern Recognition (2005).  
  39. P. Wang and Q. Ji, Learning discriminant features for multi-view face and eye detection, in Proceedings of IEEE on Computer Vision and Pattern Recognition (2005).  
  40. X. Wang and X. Tang, A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Machine Intell.26 (2004) 1222–1228.  
  41. B. Zhang, X. Chen, S. Shan and W. Gao, Nonlinear face recognition based on maximum average margin criterion, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05) (2005).  
  42. Y. Zhang and A.M. Martinez, Recognizing imprecisely localized, partially occluded and expression variant faces from a single sample per class, in Proceedings of International Conference on Pattern Recognition (ICPR 2004) (2004).  
  43. W. Zhao, R. Chellappa and A. Krishnaswamy, Discriminant analysis of principal components for face recognition. In H. Wechsler and Huang [18] 73–85.  
  44. W. Zhao, R. Chellappa, P.J. Phillips and A.Rosenfeld, Face recognition: A literature survey. ACM, Computing Surveys35 (2003) 399–458.  
  45. Z.H. Zhou and X. Geng, Projection functions for eye detection. Pattern Recognition Journal37 (2004) 1049–1056.  

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.