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 localization, face recognition, Haar wavelets, support vector machines
@article{ITA_2006__40_2_123_0, author = {Campadelli, Paola and Lanzarotti, Raffaella and Lipori, Giuseppe}, title = {Eye localization for face recognition}, journal = {RAIRO - Theoretical Informatics and Applications - Informatique Th\'eorique et Applications}, pages = {123--139}, publisher = {EDP-Sciences}, volume = {40}, number = {2}, year = {2006}, doi = {10.1051/ita:2006006}, mrnumber = {2252632}, zbl = {1112.68112}, language = {en}, url = {http://www.numdam.org/articles/10.1051/ita:2006006/} }
TY - JOUR AU - Campadelli, Paola AU - Lanzarotti, Raffaella AU - Lipori, Giuseppe TI - Eye localization for face recognition JO - RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications PY - 2006 SP - 123 EP - 139 VL - 40 IS - 2 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/ita:2006006/ DO - 10.1051/ita:2006006 LA - en ID - ITA_2006__40_2_123_0 ER -
%0 Journal Article %A Campadelli, Paola %A Lanzarotti, Raffaella %A Lipori, Giuseppe %T Eye localization for face recognition %J RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications %D 2006 %P 123-139 %V 40 %N 2 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/ita:2006006/ %R 10.1051/ita:2006006 %G en %F ITA_2006__40_2_123_0
Campadelli, Paola; Lanzarotti, Raffaella; Lipori, Giuseppe. Eye localization for face recognition. RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications, Volume 40 (2006) no. 2, pp. 123-139. doi : 10.1051/ita:2006006. http://www.numdam.org/articles/10.1051/ita:2006006/
[1] Face recognition with image sets using manifold density divergence, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05) (2005).
, , , and ,[2] A face recognition system based on automatically determined facial fiducial points. Pattern Recognition (2006) 432-443.
, and ,[3] The CSU face identification evaluation system. its purpose, features, and structure. Machine vision and applications 16 (2005) 128-138. | Zbl
, , and ,[4] Fiducial point localization in color images of face foregrounds. Image Vision Comput. J. 22 (2004) 863-872.
and ,[5] 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.
, and ,[6] Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 4-13.
, , and ,[7] An evaluation of multimodal 2d+3d face biometrics. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 619-624.
, and ,[8] Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 26 (2004) 572-581.
,[9] An introduction to support vector machines. Cambridge University Press (2000). | Zbl
and ,[10] A comparison of shape constrained facial feature detectors. Proceedings International conference on Automatic Face and Gesture Recognition (2004).
and ,[11] The BANCA Database. http://www.ee.surrey.ac.uk/Research/VSSP/banca/.
[12] The BioID Database. http://www.humanscan.de/support/downloads/facedb.php.
[13] The Face Recogniton Grand Challenge Database. http://www.frvt.org/FRGC/.
[14] The FERET Database. http://www.itl.nist.gov/iad/humanid/feret/.
[15] The XM2VTS Database. http://www.ee.surrey.ac.uk/Research/VSSP/xm2vtsdb/.
[16] 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).
, , , and ,[17] Feature-based detection of facial landmarks from neutral and expressive facial images. IEEE Trans. Pattern Anal. Machine Intell. 28 (2006) 135-139.
and ,[18] Face Recognition: from theory to applications. Springer-Verlag (1998). | Zbl
, , , and Eds.[19] Feature-based affine invariant localization of faces. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 1490-1495.
, , , , and .[20] INRIA. http://www-prima.inrialpes.fr/FGnet/data/07-XM2VTS/.
[21] Robust face detection using the hausdorff distance. Lect. Notes Comput. Sci. 2091 (2001) 212-227. | Zbl
, and ,[22] Special issue: eye detection and tracking. Comput. Vision Image Understanding 98 (2005) 1-3.
, , and ,[23] SVMlight package implementation. http://svmlight.joachims.org/.
,[24] Effective representation using ica for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Machine Intell. 27 (2005) 1977-1981.
, , and ,[25] Robust precise eye location under probabilistic framework, in Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR 04) (2004).
, , and ,[26] A bayesian similarity measure for direct image matching, in International Conference on Pattern Recognition, Vienna, Austria (1996).
, and ,[27] 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] Synergistic face detection and pose estimation with energy-based models (2005) 1017-1024.
, and ,[29] Trainable pedestrian detection, in Proceedings of International Conference on Image Processing (1999) 35-39.
and ,[30] The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Machine Intell. 22.
, , and ,[31] 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).
and ,[32] Object detection using the statistic of parts. Int. J. Comput. Vision 56 (2004) 151-177.
and ,[33] 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.
, , and ,[34] Robust precise eye location by adaboost and svm techniques, in Proceedings of the International Symposium on Neural Networks (2005) 93-98. | Zbl
, , , and ,[35] Face recognition using eigenfaces. J. Cognitive Neuroscience 3 (1991).
and ,[36] Vapnik, The nature of statistical learning theory. Springer (1995). | MR | Zbl
[37] Robust real time object detection. Inter. J. Comput. Vision 57 (2004) 137-154.
and ,[38] Automatic eye detection and its validation, in Proceedings of the Workshop FRGC in the IEEE conference on Computer Vision and Pattern Recognition (2005).
, , and ,[39] Learning discriminant features for multi-view face and eye detection, in Proceedings of IEEE on Computer Vision and Pattern Recognition (2005).
and ,[40] A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Machine Intell. 26 (2004) 1222-1228.
and ,[41] Nonlinear face recognition based on maximum average margin criterion, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05) (2005).
, , and ,[42] 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).
and ,[43] Discriminant analysis of principal components for face recognition. In H. Wechsler and Huang [18] 73-85.
, and ,[44] Face recognition: A literature survey. ACM, Computing Surveys 35 (2003) 399-458.
, , and ,[45] Projection functions for eye detection. Pattern Recognition Journal 37 (2004) 1049-1056. | Zbl
and ,Cited by Sources: