Face Recognition using Weighted Distance Transform

International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN. International E-Bulletin: Information/News regarding: Academics and Research

Face Recognition using Weighted Distance Transform

Author Affiliations

¹ Department of Computer Science, Federal Urdu University of Arts, Sciences and Technology, Karachi, PAKISTAN

Res. J. Recent Sci., Volume 3, Issue (9), Pages 19-25, September,2 (2014)

Abstract

In recent years the task of ecognizing human face with the help of a machine has acquired a significant attention among researchers. A wide range of commercial and law enforcement applications largely accounts for this developing trend. Although current face ecognition systems have reached a certain maturity level they are still far away from the capability of human perceptual system. In a 2D image the unique facial appearance normally consists of intensity curvatures which may be incorporated for ecognizing human face. This work inestigates the use of weighted distance transform to bring improvements in face ecognition. The weighted distance transform takes into account both the spatial distance among pixels and the local intensity variations. A standard face dataset is used to alidate the proposed method. The obtained results are comparable to the state-of-the-art face recognition techniques.

References

Phillips P. J., Grother P., Micheals R., Blackburn D.M., Tabassi E. and Bone M., Face recognition vendor test 2002, Anal. And Mod. of Faces and Gestures 2003, AMFG 2003, IEEE International Workshop, (2003)
Phillips P.J., Moon H., Rizvi S.A. and Rauss P.J., The feret evaluation methodology for face-recognition algorithms, Pattern Anal. and Machine Intelligence, IEEE Transactions, 22(10), 1090–1104 (2000)
Phillips P.J., Wechsler H., Huang J. and Rauss P.J., The feret database and evaluation procedure for face-recognition algorithms, Image and vision comput, 16(5), 295–306 (1998)
Zhao W., Chellappa R., Phillips P.J. and Rosenfeld A., Face recognition: A literature survey, Acm Comput. Surveys (CSUR),35(4), 399–458 (2003)
Hafed Z.M. and Levine M.D., Face recognition using the discrete cosine transform, Int. J. of Comp. Vision, 43(3), 167–188 (2001)
Sadeghi R., A comparative face recognition algorithm for dark places, Res. J. of Rec. Sci.,2(9), 92–94 (2013)
A. Kokou M., and Antoine V., Shape characterization on phase microscopy images using a dispersion indicator: Application to amoeba cells, Res. J. of Comp. and Info. Tech. Sci.,1(5), 8–12 (2013)
Criminisi A., Sharp T., and Blake A., Geos: Geodesic image segmentation, Computer Vision–ECCV, Springer, 99–112, (2008)
Borgefors G., Distance transformations in digital images, Computer vision, graphics, and image processing, 34(3), 344–371 (1986)
Sethian J.A., Fast marching methods, SIAM review, 41(2), 199–235 (1999)
Prasad M., Panda S., Deepthi G. and Anisha V., Face recognition using pca and feed forward neural networks, Int. J. of Comp. Sci. and Tele.,2(8), 79–82 (2011)
Perlibakas V., Face recognition using principal component analysis and wavelet packet decomposition, Informatica, 15(2), 243–250 (2004)
Er M.J., Chen W. and Wu S., High-speed face recognition based on discrete cosine transform and rbf neural networks, Neural Networks, EEE Transactions, 16(3), 679–691 (2005)
Lai J.H., Yuen P.C. and Feng G.C., Face recognition using holistic fourier invariant features, Pattern Recognition, 34(1), 95–109 (2001)
Tjahyadi R., Liu W. and Venkatesh S., Application of the dct energy histogram for face recognition, ICITA 2004, Proc. of the Second International Conference on Information Technology and Applications, 314–319, IEEE, (2004)
Tjahyadi R., Liu W., An S. and Venkatesh S., Face recognition via the overlapping energy histogram, IJCAI, 2891–2896 (2007)
Lay J.A. and Guan L., Image retrieval based on energy histograms of the low frequency dct coefficients, Acoustics, Speech and Signal Processing, IEEE International Conference, , 3009–3012 (1999)
Muhammad Sharif S.M.M.R. and Shah J.H., Sub-holistic hidden markov model for face recognition, Res. J. of Rec. Sci.,2(5), 10–14 (2013)
Sethian J.A., A fast marching level set method for monotonically advancing fronts, Proc. of the National Academy of Sciences, 93(4), 1591–1595 (1996)

[ref1] Phillips P. J., Grother P., Micheals R., Blackburn D.M., Tabassi E. and Bone M., Face recognition vendor test 2002, Anal. And Mod. of Faces and Gestures 2003, AMFG 2003, IEEE International Workshop, (2003)

[ref2] Phillips P.J., Moon H., Rizvi S.A. and Rauss P.J., The feret evaluation methodology for face-recognition algorithms, Pattern Anal. and Machine Intelligence, IEEE Transactions, 22(10), 1090–1104 (2000)

[ref3] Phillips P.J., Wechsler H., Huang J. and Rauss P.J., The feret database and evaluation procedure for face-recognition algorithms, Image and vision comput, 16(5), 295–306 (1998)

[ref4] Zhao W., Chellappa R., Phillips P.J. and Rosenfeld A., Face recognition: A literature survey, Acm Comput. Surveys (CSUR),35(4), 399–458 (2003)

[ref5] Hafed Z.M. and Levine M.D., Face recognition using the discrete cosine transform, Int. J. of Comp. Vision, 43(3), 167–188 (2001)

[ref6] Sadeghi R., A comparative face recognition algorithm for dark places, Res. J. of Rec. Sci.,2(9), 92–94 (2013)

[ref7] A. Kokou M., and Antoine V., Shape characterization on phase microscopy images using a dispersion indicator: Application to amoeba cells, Res. J. of Comp. and Info. Tech. Sci.,1(5), 8–12 (2013)

[ref8] Criminisi A., Sharp T., and Blake A., Geos: Geodesic image segmentation, Computer Vision–ECCV, Springer, 99–112, (2008)

[ref9] Borgefors G., Distance transformations in digital images, Computer vision, graphics, and image processing, 34(3), 344–371 (1986)

[ref10] Sethian J.A., Fast marching methods, SIAM review, 41(2), 199–235 (1999)

[ref11] Prasad M., Panda S., Deepthi G. and Anisha V., Face recognition using pca and feed forward neural networks, Int. J. of Comp. Sci. and Tele.,2(8), 79–82 (2011)

[ref12] Perlibakas V., Face recognition using principal component analysis and wavelet packet decomposition, Informatica, 15(2), 243–250 (2004)

[ref13] Er M.J., Chen W. and Wu S., High-speed face recognition based on discrete cosine transform and rbf neural networks, Neural Networks, EEE Transactions, 16(3), 679–691 (2005)

[ref14] Lai J.H., Yuen P.C. and Feng G.C., Face recognition using holistic fourier invariant features, Pattern Recognition, 34(1), 95–109 (2001)

[ref15] Tjahyadi R., Liu W. and Venkatesh S., Application of the dct energy histogram for face recognition, ICITA 2004, Proc. of the Second International Conference on Information Technology and Applications, 314–319, IEEE, (2004)

[ref16] Tjahyadi R., Liu W., An S. and Venkatesh S., Face recognition via the overlapping energy histogram, IJCAI, 2891–2896 (2007)

[ref17] Lay J.A. and Guan L., Image retrieval based on energy histograms of the low frequency dct coefficients, Acoustics, Speech and Signal Processing, IEEE International Conference, , 3009–3012 (1999)

[ref18] Muhammad Sharif S.M.M.R. and Shah J.H., Sub-holistic hidden markov model for face recognition, Res. J. of Rec. Sci.,2(5), 10–14 (2013)

[ref19] Sethian J.A., A fast marching level set method for monotonically advancing fronts, Proc. of the National Academy of Sciences, 93(4), 1591–1595 (1996)