Face Detection using Gabor Wavelets and Neural Networks

This paper proposes new hybrid approaches for face recognition. Gabor wavelets representation of face images is an effective approach for both facial action recognition and face identification. Perform dimensionality reduction and linear discriminate analysis on the down sampled Gabor wavelet faces can increase the discriminate ability. Nearest feature space is extended to various similarity measures. In our experiments, proposed Gabor wavelet faces combined with extended neural net feature space classifier shows very good performance, which can achieve 93 % maximum correct recognition rate on ORL data set without any preprocessing step.

Authors:

• Hossein Sahoolizadeh
• Davood Sarikhanimoghadam
• Hamid Dehghani

Keywords:

• Face detection
• Neural Networks
• Multi-layer Perceptron
• Gabor wavelets.

References:

[1] R. W. Chellappa, C.L.; Sirohey, S., "Human and machine recognition of
faces: a survey," Proceedings of the IEEE, vol. 83, pp. 705-741, 1995.
[2] M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces,"
presented at Proceedings CVPR -91., 1991. [3] P. N. Belhumeur, J. P.
Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition
using class specific linear projection," PAMI, IEEE Trans. on, vol. 19,
1997.
[3] A. M. Martinez and A. C. Kak, "PCA versus LDA," PAMI, IEEE Trans.
on, vol. 23, pp. 228-233, 2001.
[4] C. Liu and H. Wechsler, "Gabor feature based classification using the
enhanced fisher linear discriminant model for face recognition," Image
Processing, IEEE Trans. on, vol. 11, pp. 467-476, 2002.
[5] S. Z. Li and J. Lu, "Face recognition using the nearest feature line
method," Neural Networks, IEEE Trans. on, vol. 10, pp. 439-443, 1999.
[6] J.-T. Chien and C.-C. Wu, "Discriminant waveletfaces and nearest
feature classifiers for face recognition," PAMI, IEEE Trans. on, vol. 24,
pp. 1644-1649, 2002.
[7] ORL web site: www.uk.research.att.com/facedatabase.html.
[8] T. S. Lee, "Image representation using 2D Gabor wavelets," PAMI,
IEEE Trans. on, vol. 18, pp. 959-971, 1996.
[9] M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. von der
Malsburg, R. P. Wurtz, and W. Konen, "Distortion invariant object
recognition in the dynamic link architecture," Computers, IEEE Trans.
on, vol. 42, pp. 300-311, 1993.