Face Recognition Using Discrete Orthogonal Hahn Moments
One of the most critical decision points in the design of a
face recognition system is the choice of an appropriate face representation.
Effective feature descriptors are expected to convey sufficient, invariant
and non-redundant facial information. In this work we propose a set of
Hahn moments as a new approach for feature description. Hahn moments
have been widely used in image analysis due to their invariance, nonredundancy
and the ability to extract features either globally and locally.
To assess the applicability of Hahn moments to Face Recognition we
conduct two experiments on the Olivetti Research Laboratory (ORL)
database and University of Notre-Dame (UND) X1 biometric collection.
Fusion of the global features along with the features from local facial
regions are used as an input for the conventional k-NN classifier. The
method reaches an accuracy of 93% of correctly recognized subjects for
the ORL database and 94% for the UND database.
[1] Bledsoe, W. W. The Model Method in Facial Recognition, Technical Report
PRI 15, Panoramic Research, Inc., Palo Alto, California, 1964
[2] M. Kirby and L. Sirovich. Application of the Karhunen-Loeve procedure for
the characterization of human faces. IEEE Transactions on Pattern analysis
and Machine Intelligence 12, 1990
[3] Jolliffe, I. T. Principal Component Analysis. Springer-Verlag. p. 487, 1986
[4] A. Pentland, T. Starner, N. Etcoff, N. Masoiu, O. Oliyide, and M. Turk.
Experiments with Eigenfaces. Proc. Looking at People Workshop Int’l Joint
Conf. Artifical Intelligence, 1993
[5] McLachlan, G. J. Discriminant Analysis and Statistical Pattern Recognition.
Wiley Interscience, 2004
[6] Marian Stewart Bartlett, Javier R. Movellan, and Terrence J. Sejnowski Face
Recognition by Independent Component Analysis. IEEE Trans Neural Netw.
vol. 13(6), pages 14501464, 2002
[7] Laurenz Wiskott, Jean-Marc Fellous,Norbert Kruger, and Christoph von der
Malsburg. Face Recognition by Elastic Bunch Graph Matching. Intelligent
Biometric Techniques in Fingerprint and Face Recognition, Chapter 11, pages
355-396, 1999
[8] Arnold F. Nikiforov, Vasilii B. Uvarov, Sergei K. Suslov. Classical Orthogonal
Polynomials of a Discrete Variable. Springer Series in Computational Physics,
1991
[9] Sajad Farokhi, Siti Mariyam Shamsuddin, Jan Flusser, Usman Ullah Sheikh.
Assessment of Time-Lapse in Visible and Thermal Face Recognition. World
Academy of Science, Engineering and Technology Vol.6, 2012
[10] Mukundan, R., Ong S.H., Lee P.A. Image analysis by Tchebichef moments.
Image Processing, IEEE Transactions on, Vol.10 , Issue: 9, 2001
[11] Yap P.T., Paramesran R., Ong S.H. Image analysis by Krawtchouk moments.
IEEE Trans Image Process. Vol. 12, pages 1367-77, 2003
[12] Sri dhar Dasari, Sridhar Dasari. Face Recognition using Tchebichef
Moments.International Journal of Infromation and Network Security, Vol 1,
No 4, 2012
[13] J Sheeba Rani, D Devaraj. Face recognition using Krawtchouk moment.
Sadhana, Vol. 37, Issue 4, pages 441-460, 2012 [14] Pew-Thian Yap, Paramesran, R., Seng-Huat Ong. Image Analysis Using Hahn
Moments. Pattern Analysis and Machine Intelligence, IEEE Transactions on,
Vol. 29, Issue 11, 2007
[15] Ferdinando Samaria, Andy Harter. Parameterisation of a Stochastic Model
for Human Face Identification. Proceedings of 2nd IEEE Workshop on
Applications of Computer Vision, Sarasota FL, 1994
[16] P. J. Flynn, K. W. Bowyer, and P. J. Phillips. Assessment of time dependency
in face recognition: An initial study. Audio and Video-Based Biometric Person
Authentication, pages 44-51, 2003.
[17] X. Chen, P. J. Flynn, and K. W. Bowyer. Visible -light and Infrared Face
Recognition. ACM Workshop on Multimodal User Authentication, pages 48-
55, 2003
[18] Jan Flusser, Barbara Zitova, Tomas Suk. Moments and Moment Invariants in
Pattern Recognition. ISBN: 978-0-470-69987-4, page 6, 2009
[19] Jian Zhou, Huazhong Shu, Hongqing Zhu, Christine Toumoulin, and Limin
Luo. Image Analysis by Discrete Orthogonal Hahn Moments. Lecture Notes
in Computer Science, Vol. 3656, pages 524-531, 2005
[20] Paul Viola, Michael Jones. Robust Real-time Object Detection. International
Journal of Computer Vision, 2001
[21] Andrew Fitzgibbon, Maurizio Pilu, and Robert B. Fisher. Direct Least
Square Fitting of Ellipses. Proc. of the 13th Internation Conference on Pattern
Recognition, pages 253257, Vienna, 1996
[22] http://www.cl.cam.ac.uk/research/dtg/attarchive /facedatabase.html.
[23] Sayyouri M, Hmimid A, Qjidaa H. Improving the performance of image
classification by Hahn moment invariants. Journal of the Optical Society of
America, Vol. 30, Issue 11, pages 2381-2394, 2013
[1] Bledsoe, W. W. The Model Method in Facial Recognition, Technical Report
PRI 15, Panoramic Research, Inc., Palo Alto, California, 1964
[2] M. Kirby and L. Sirovich. Application of the Karhunen-Loeve procedure for
the characterization of human faces. IEEE Transactions on Pattern analysis
and Machine Intelligence 12, 1990
[3] Jolliffe, I. T. Principal Component Analysis. Springer-Verlag. p. 487, 1986
[4] A. Pentland, T. Starner, N. Etcoff, N. Masoiu, O. Oliyide, and M. Turk.
Experiments with Eigenfaces. Proc. Looking at People Workshop Int’l Joint
Conf. Artifical Intelligence, 1993
[5] McLachlan, G. J. Discriminant Analysis and Statistical Pattern Recognition.
Wiley Interscience, 2004
[6] Marian Stewart Bartlett, Javier R. Movellan, and Terrence J. Sejnowski Face
Recognition by Independent Component Analysis. IEEE Trans Neural Netw.
vol. 13(6), pages 14501464, 2002
[7] Laurenz Wiskott, Jean-Marc Fellous,Norbert Kruger, and Christoph von der
Malsburg. Face Recognition by Elastic Bunch Graph Matching. Intelligent
Biometric Techniques in Fingerprint and Face Recognition, Chapter 11, pages
355-396, 1999
[8] Arnold F. Nikiforov, Vasilii B. Uvarov, Sergei K. Suslov. Classical Orthogonal
Polynomials of a Discrete Variable. Springer Series in Computational Physics,
1991
[9] Sajad Farokhi, Siti Mariyam Shamsuddin, Jan Flusser, Usman Ullah Sheikh.
Assessment of Time-Lapse in Visible and Thermal Face Recognition. World
Academy of Science, Engineering and Technology Vol.6, 2012
[10] Mukundan, R., Ong S.H., Lee P.A. Image analysis by Tchebichef moments.
Image Processing, IEEE Transactions on, Vol.10 , Issue: 9, 2001
[11] Yap P.T., Paramesran R., Ong S.H. Image analysis by Krawtchouk moments.
IEEE Trans Image Process. Vol. 12, pages 1367-77, 2003
[12] Sri dhar Dasari, Sridhar Dasari. Face Recognition using Tchebichef
Moments.International Journal of Infromation and Network Security, Vol 1,
No 4, 2012
[13] J Sheeba Rani, D Devaraj. Face recognition using Krawtchouk moment.
Sadhana, Vol. 37, Issue 4, pages 441-460, 2012 [14] Pew-Thian Yap, Paramesran, R., Seng-Huat Ong. Image Analysis Using Hahn
Moments. Pattern Analysis and Machine Intelligence, IEEE Transactions on,
Vol. 29, Issue 11, 2007
[15] Ferdinando Samaria, Andy Harter. Parameterisation of a Stochastic Model
for Human Face Identification. Proceedings of 2nd IEEE Workshop on
Applications of Computer Vision, Sarasota FL, 1994
[16] P. J. Flynn, K. W. Bowyer, and P. J. Phillips. Assessment of time dependency
in face recognition: An initial study. Audio and Video-Based Biometric Person
Authentication, pages 44-51, 2003.
[17] X. Chen, P. J. Flynn, and K. W. Bowyer. Visible -light and Infrared Face
Recognition. ACM Workshop on Multimodal User Authentication, pages 48-
55, 2003
[18] Jan Flusser, Barbara Zitova, Tomas Suk. Moments and Moment Invariants in
Pattern Recognition. ISBN: 978-0-470-69987-4, page 6, 2009
[19] Jian Zhou, Huazhong Shu, Hongqing Zhu, Christine Toumoulin, and Limin
Luo. Image Analysis by Discrete Orthogonal Hahn Moments. Lecture Notes
in Computer Science, Vol. 3656, pages 524-531, 2005
[20] Paul Viola, Michael Jones. Robust Real-time Object Detection. International
Journal of Computer Vision, 2001
[21] Andrew Fitzgibbon, Maurizio Pilu, and Robert B. Fisher. Direct Least
Square Fitting of Ellipses. Proc. of the 13th Internation Conference on Pattern
Recognition, pages 253257, Vienna, 1996
[22] http://www.cl.cam.ac.uk/research/dtg/attarchive /facedatabase.html.
[23] Sayyouri M, Hmimid A, Qjidaa H. Improving the performance of image
classification by Hahn moment invariants. Journal of the Optical Society of
America, Vol. 30, Issue 11, pages 2381-2394, 2013
@article{"International Journal of Information, Control and Computer Sciences:70758", author = "Fatima Akhmedova and Simon Liao", title = "Face Recognition Using Discrete Orthogonal Hahn Moments", abstract = "One of the most critical decision points in the design of a
face recognition system is the choice of an appropriate face representation.
Effective feature descriptors are expected to convey sufficient, invariant
and non-redundant facial information. In this work we propose a set of
Hahn moments as a new approach for feature description. Hahn moments
have been widely used in image analysis due to their invariance, nonredundancy
and the ability to extract features either globally and locally.
To assess the applicability of Hahn moments to Face Recognition we
conduct two experiments on the Olivetti Research Laboratory (ORL)
database and University of Notre-Dame (UND) X1 biometric collection.
Fusion of the global features along with the features from local facial
regions are used as an input for the conventional k-NN classifier. The
method reaches an accuracy of 93% of correctly recognized subjects for
the ORL database and 94% for the UND database.", keywords = "Face Recognition, Hahn moments, Recognition-by-parts,
Time-lapse.", volume = "9", number = "6", pages = "1550-7", }
