Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-14T13:21:42.199Z Has data issue: false hasContentIssue false
  • English
  • Français

Eye localization for face recognition

Published online by Cambridge University Press:  20 July 2006

Paola Campadelli ,
Raffaella Lanzarotti  and
Giuseppe Lipori
Paola Campadelli
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico, 39/41 20135 Milano, Italy; [email protected], [email protected], [email protected]
Raffaella Lanzarotti
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico, 39/41 20135 Milano, Italy; [email protected], [email protected], [email protected]
Giuseppe Lipori
Affiliation:
Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, via Comelico, 39/41 20135 Milano, Italy; [email protected], [email protected], [email protected]
Get access

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.

Keywords

Eye localizationface recognitionHaar waveletssupport vector machines.
Type
Research Article
Information
RAIRO - Theoretical Informatics and Applications , Volume 40 , Issue 2: Alberto Bertoni: Climbing summits , April 2006 , pp. 123 - 139
Copyright
© EDP Sciences, 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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).
S. Arca, P. Campadelli and R. Lanzarotti, A face recognition system based on automatically determined facial fiducial points. Pattern Recognition (2006) 432–443.
Beveridge, J.R., Bolme, D., Draper, B.A. and Teixeira, M., The CSU face identification evaluation system. its purpose, features, and structure. Machine vision and applications 16 (2005) 128–138. CrossRef
Campadelli, P. and Lanzarotti, R., Fiducial point localization in color images of face foregrounds. Image Vision Comput. J. 22 (2004) 863–872. CrossRef
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.
Cevikalp, H., Neamtu, M., Wilkes, M. and Barkaba, A., Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 4–13. CrossRef
Chang, K.I., Bowyer, K.W. and Flynn, P.J., An evaluation of multimodal 2d+3d face biometrics. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 619–624. CrossRef
Liu, C., Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 26 (2004) 572–581.
N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines. Cambridge University Press (2000).
D. Cristinacce and T.F. Cootes, A comparison of shape constrained facial feature detectors. Proceedings International conference on Automatic Face and Gesture Recognition (2004).
The BANCA Database. http://www.ee.surrey.ac.uk/Research/VSSP/banca/.
The BioID Database. http://www.humanscan.de/support/downloads/facedb.php.
The Face Recogniton Grand Challenge Database. http://www.frvt.org/FRGC/.
The FERET Database. http://www.itl.nist.gov/iad/humanid/feret/.
The XM2VTS Database. http://www.ee.surrey.ac.uk/Research/VSSP/xm2vtsdb/.
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).
Gizatdinova, Y. and Surakka, V., Feature-based detection of facial landmarks from neutral and expressive facial images. IEEE Trans. Pattern Anal. Machine Intell. 28 (2006) 135–139. CrossRef
V. Bruce, F. Fogelman Soulie, H. Wechsler, P.J. Phillips and T. Huang Eds. Face Recognition: from theory to applications. Springer-Verlag (1998).
Hamouz, M., Kittler, J., Kamarainen, J.K., Paalanen, P., Kälviäinen, H. and Matas, J.. Feature-based affine invariant localization of faces. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 1490–1495. CrossRef
INRIA. http://www-prima.inrialpes.fr/FGnet/data/07-XM2VTS/.
Jesorsky, O., Kirchberg, K.J. and Frischholz, R.W., Robust face detection using the hausdorff distance. Lect. Notes Comput. Sci. 2091 (2001) 212–227.
Ji, Q., Wechsler, H., Duchowski, A. and Flickner, M., Special issue: eye detection and tracking. Comput. Vision Image Understanding 98 (2005) 1–3. CrossRef
T. Joachims, SVMlight package implementation. http://svmlight.joachims.org/.
Kim, J., Choi, J., Yi, J. and Turk, M., Effective representation using ica for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 1977–1981.
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).
B. Moghaddam, C. Nastar and A. Pentland, A bayesian similarity measure for direct image matching, in International Conference on Pattern Recognition, Vienna, Austria (1996).
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.
M. Osadchy, M.L. Miller and Y. LeCun, Synergistic face detection and pose estimation with energy-based models (2005) 1017–1024.
C.P. Papageorgiou and T. Poggio, Trainable pedestrian detection, in Proceedings of International Conference on Image Processing (1999) 35–39.
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.
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).
Schneiderman, H. and Kanade, T., Object detection using the statistic of parts. Int. J. Comput. Vision 56 (2004) 151–177. CrossRef
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.
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.
M. Turker and A. Pentland, Face recognition using eigenfaces. J. Cognitive Neuroscience 3 (1991).
Vapnik, The nature of statistical learning theory. Springer (1995).
Viola, P. and Jones, M., Robust real time object detection. Inter. J. Comput. Vision 57 (2004) 137–154. CrossRef
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).
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).
Wang, X. and Tang, X., A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Machine Intell. 26 (2004) 1222–1228. CrossRef
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).
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).
W. Zhao, R. Chellappa and A. Krishnaswamy, Discriminant analysis of principal components for face recognition. In H. Wechsler and Huang [18] 73–85.
W. Zhao, R. Chellappa, P.J. Phillips and A.Rosenfeld, Face recognition: A literature survey. ACM, Computing Surveys 35 (2003) 399–458.
Zhou, Z.H. and Geng, X., Projection functions for eye detection. Pattern Recognition Journal 37 (2004) 1049–1056. CrossRef