Robust Face Recognition using AAM and Gabor Features
In this paper, we propose a face recognition algorithm
using AAM and Gabor features. Gabor feature vectors which are well
known to be robust with respect to small variations of shape, scaling,
rotation, distortion, illumination and poses in images are popularly
employed for feature vectors for many object detection and
recognition algorithms. EBGM, which is prominent among face
recognition algorithms employing Gabor feature vectors, requires
localization of facial feature points where Gabor feature vectors are
extracted. However, localization method employed in EBGM is based
on Gabor jet similarity and is sensitive to initial values. Wrong
localization of facial feature points affects face recognition rate. AAM
is known to be successfully applied to localization of facial feature
points. In this paper, we devise a facial feature point localization
method which first roughly estimate facial feature points using AAM
and refine facial feature points using Gabor jet similarity-based facial
feature localization method with initial points set by the rough facial
feature points obtained from AAM, and propose a face recognition
algorithm using the devised localization method for facial feature
localization and Gabor feature vectors. It is observed through
experiments that such a cascaded localization method based on both
AAM and Gabor jet similarity is more robust than the localization
method based on only Gabor jet similarity. Also, it is shown that the
proposed face recognition algorithm using this devised localization
method and Gabor feature vectors performs better than the
conventional face recognition algorithm using Gabor jet
similarity-based localization method and Gabor feature vectors like
EBGM.
[1] L. O'Gorman, "Comparing passwords, tokens, and biometrics for user
authentication," Proceedings of the IEEE Volume 91, Issue 12, Dec 2003
Page(s):2021 - 2040
[2] W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, "Face Recognition:
A Literature Survey," ACM Computing Surveys, pp. 399-458, 2003.
[3] S.Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004.
[4] Y. Adini, Y. Moses, and S. Ullman, "Face Recognition: The problem of
compensating for changes in illumination direction," IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 721-732,
July 1997.
[5] M. Turk and A. Pentland, "Face Recognition using Eigenfaces,"
Proceedings of IEEE Computer Vision and Pattern Recognition, pp.
586-590, Maui, Hawaii, Dec. 1991.
[6] V. Belhumeur, J. Hespanha, and D. Kriegman. "Eigenfaces vs.
Fisherfaces: Recognition using class specic linear projection," IEEE
Transactions on Pattern Analysis and Machine Intelligence, 19(7), pp.
711-720, July 1997.
[7] M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face Recognition by
Independent Component Analysis, IEEE Trans. on Neural Networks, Vol.
13, No. 6, pp. 1450-1464 , November 2002.
[8] P. S. Penev, Local feature analysis: A Statistical Theory for Information
Representation and Transmission, Ph.D. Thesis, The Rockefeller
University, 1998.
[9] A. L. Yuille, "Deformable Templates for Face Recognition," J. Cognitive
Neurosci., vol. 3, no. 1, pp 59-70, 1991.
[10] L. Wiskott, J. M. Fellous, N. Kuiger, C. von der Malsburg, "Face
Recognition by Elastic Bunch Graph Matching," Pattern Analysis and
Machine Intelligence, IEEE Transactions on Vol. 19, pp. 775 - 779, July
1997.
[11] David Bolme, Elastic Bunch Graph Matching, Masters Thesis, CSU
Computer Science Department June 2003
[12] T. F. Cootes, D. J. Edwards, and S. J. Taylor, "Active Appearance
Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp.
681-685, Jun. 2001.
[13] M. B. Stegmann, B. K. Ersboll, R. Larsen, "FAME -- A Flexible
Appearance Modelling Environment," IEEE Transactions on Medical
Imaging, Vol. 22, Iss.10, pp. 1319 - 1331, Oct. 2003.
[14] V. Blanz and T. Vetter, "Face Recognition based on Fitting a 3D
Morphable Model," IEEE Trans on Pattern Analysis and Machine
Intelligence 25 (9), pp 1063-1074, 2003.
[15] J.-K. Kamarainen, V. Kyrki, H.Kalviainen, "Invariance Properties of
Gabor filter-based features - overview and applications," Image
Processing, IEEE Transactions on image processing, Vol. 15, Issue 5,
pp.1088 - 1099, May 2006.
[16] P. Wang, M.B Green, Ji Qiang, J. Wayman., "Automatic Eye Detection
and Its Validation," Computer Vision and Pattern Recognition, 2005
IEEE Computer Society Conference, Vol. 3, pp. 164 - 172, June 2005.
[17] J. C. Gower, "Generalized Procrustes Analysis," Psychometrika,
40:33-51, 1975.
[18] D. T. Lee and B. J. Schachter, "Two Algorithms for Constructing a
Delaunay Triangulation," Int. J. Computer Information Sci. 9,
pp.219-242, 1980.
[19] Rainer Lienhart and Jochen Maydt, "An Extended Set of Haar-like
Features for Rapid Object Detection," IEEE ICIP 2002, Vol. 1, pp.
900-903, Sep. 2002
[20] T. Kawaguchi, D. Hidaka and M. Rizon, "Robust Extraction of Eyes from
Face," 15th Int-l Conf. on Pattern Recognition, Vol. 1. pp. 1109 - 1114,
Sept. 2000.
[21] AAM-API , http://www2.imm.dtu.dk/~aam/
[1] L. O'Gorman, "Comparing passwords, tokens, and biometrics for user
authentication," Proceedings of the IEEE Volume 91, Issue 12, Dec 2003
Page(s):2021 - 2040
[2] W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, "Face Recognition:
A Literature Survey," ACM Computing Surveys, pp. 399-458, 2003.
[3] S.Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004.
[4] Y. Adini, Y. Moses, and S. Ullman, "Face Recognition: The problem of
compensating for changes in illumination direction," IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 721-732,
July 1997.
[5] M. Turk and A. Pentland, "Face Recognition using Eigenfaces,"
Proceedings of IEEE Computer Vision and Pattern Recognition, pp.
586-590, Maui, Hawaii, Dec. 1991.
[6] V. Belhumeur, J. Hespanha, and D. Kriegman. "Eigenfaces vs.
Fisherfaces: Recognition using class specic linear projection," IEEE
Transactions on Pattern Analysis and Machine Intelligence, 19(7), pp.
711-720, July 1997.
[7] M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face Recognition by
Independent Component Analysis, IEEE Trans. on Neural Networks, Vol.
13, No. 6, pp. 1450-1464 , November 2002.
[8] P. S. Penev, Local feature analysis: A Statistical Theory for Information
Representation and Transmission, Ph.D. Thesis, The Rockefeller
University, 1998.
[9] A. L. Yuille, "Deformable Templates for Face Recognition," J. Cognitive
Neurosci., vol. 3, no. 1, pp 59-70, 1991.
[10] L. Wiskott, J. M. Fellous, N. Kuiger, C. von der Malsburg, "Face
Recognition by Elastic Bunch Graph Matching," Pattern Analysis and
Machine Intelligence, IEEE Transactions on Vol. 19, pp. 775 - 779, July
1997.
[11] David Bolme, Elastic Bunch Graph Matching, Masters Thesis, CSU
Computer Science Department June 2003
[12] T. F. Cootes, D. J. Edwards, and S. J. Taylor, "Active Appearance
Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp.
681-685, Jun. 2001.
[13] M. B. Stegmann, B. K. Ersboll, R. Larsen, "FAME -- A Flexible
Appearance Modelling Environment," IEEE Transactions on Medical
Imaging, Vol. 22, Iss.10, pp. 1319 - 1331, Oct. 2003.
[14] V. Blanz and T. Vetter, "Face Recognition based on Fitting a 3D
Morphable Model," IEEE Trans on Pattern Analysis and Machine
Intelligence 25 (9), pp 1063-1074, 2003.
[15] J.-K. Kamarainen, V. Kyrki, H.Kalviainen, "Invariance Properties of
Gabor filter-based features - overview and applications," Image
Processing, IEEE Transactions on image processing, Vol. 15, Issue 5,
pp.1088 - 1099, May 2006.
[16] P. Wang, M.B Green, Ji Qiang, J. Wayman., "Automatic Eye Detection
and Its Validation," Computer Vision and Pattern Recognition, 2005
IEEE Computer Society Conference, Vol. 3, pp. 164 - 172, June 2005.
[17] J. C. Gower, "Generalized Procrustes Analysis," Psychometrika,
40:33-51, 1975.
[18] D. T. Lee and B. J. Schachter, "Two Algorithms for Constructing a
Delaunay Triangulation," Int. J. Computer Information Sci. 9,
pp.219-242, 1980.
[19] Rainer Lienhart and Jochen Maydt, "An Extended Set of Haar-like
Features for Rapid Object Detection," IEEE ICIP 2002, Vol. 1, pp.
900-903, Sep. 2002
[20] T. Kawaguchi, D. Hidaka and M. Rizon, "Robust Extraction of Eyes from
Face," 15th Int-l Conf. on Pattern Recognition, Vol. 1. pp. 1109 - 1114,
Sept. 2000.
[21] AAM-API , http://www2.imm.dtu.dk/~aam/
@article{"International Journal of Information, Control and Computer Sciences:52692", author = "Sanghoon Kim and Sun-Tae Chung and Souhwan Jung and Seoungseon Jeon and Jaemin Kim and Seongwon Cho", title = "Robust Face Recognition using AAM and Gabor Features", abstract = "In this paper, we propose a face recognition algorithm
using AAM and Gabor features. Gabor feature vectors which are well
known to be robust with respect to small variations of shape, scaling,
rotation, distortion, illumination and poses in images are popularly
employed for feature vectors for many object detection and
recognition algorithms. EBGM, which is prominent among face
recognition algorithms employing Gabor feature vectors, requires
localization of facial feature points where Gabor feature vectors are
extracted. However, localization method employed in EBGM is based
on Gabor jet similarity and is sensitive to initial values. Wrong
localization of facial feature points affects face recognition rate. AAM
is known to be successfully applied to localization of facial feature
points. In this paper, we devise a facial feature point localization
method which first roughly estimate facial feature points using AAM
and refine facial feature points using Gabor jet similarity-based facial
feature localization method with initial points set by the rough facial
feature points obtained from AAM, and propose a face recognition
algorithm using the devised localization method for facial feature
localization and Gabor feature vectors. It is observed through
experiments that such a cascaded localization method based on both
AAM and Gabor jet similarity is more robust than the localization
method based on only Gabor jet similarity. Also, it is shown that the
proposed face recognition algorithm using this devised localization
method and Gabor feature vectors performs better than the
conventional face recognition algorithm using Gabor jet
similarity-based localization method and Gabor feature vectors like
EBGM.", keywords = "Face Recognition, AAM, Gabor features, EBGM.", volume = "1", number = "9", pages = "2689-5", }