The Modified Eigenface Method using Two Thresholds
A new approach is adopted in this paper based
on Turk and Pentland-s eigenface method. It was found that the
probability density function of the distance between the projection
vector of the input face image and the average projection vector of
the subject in the face database, follows Rayleigh distribution. In
order to decrease the false acceptance rate and increase the
recognition rate, the input face image has been recognized using two
thresholds including the acceptance threshold and the rejection
threshold. We also find out that the value of two thresholds will be
close to each other as number of trials increases. During the training,
in order to reduce the number of trials, the projection vectors for each
subject has been averaged. The recognition experiments using the
proposed algorithm show that the recognition rate achieves to
92.875% whilst the average number of judgment is only 2.56 times.
[1] M. Turk, A. Pentland, "Face recognition using eigenfaces", in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 1991,
pp.586-591.
[2] C. Liu, H. Wechsler, "Gabor feature based classification using the
enhanced fisher linear Discriminant model for face recognition", IEEE
Trans. Image Processing, vol.11, pp.467-476, 2002.
[3] J.-W. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Face Recognition
using kernel discriminant analysis algorithms", IEEE Trans. on Neural
Networks, vol.14, pp.117-126, 2003.
[4] H.-C. Kim, D.-J. Kim, Y.-B. Sung, S.-Y. Lee, "Face recognition using
the second-order mixture-of-eigenfaces method", Pattern Recognition,
vol.37, pp.337-349, 2004.
[5] Y. Xu, J. Y. Yang, J.-F. Lu, D.-J. Yu, "An efficient renovation on kernel
fisher discriminant analysis and face recognition experiments", Pattern
Recognition, vol.37, pp.2091-2094, 2004.
[6] A. Samal, P. A. Lyengar, "Automatic recognition and analysis of human
faces and facial expressions: a survey", Pattern Recognition, vol.25,
pp.65-77, 1992.
[7] B. Moghaddam, "Bayesian face recognition", Pattern Recognition,
vol.13, pp.1771-1782, 2000.
[8] L. Wiskott, J.M.Fellous, N.Kruger, C.Malsburg, "Face recognition by
elastic bunch graph matching", IEEE Trans. Pattern Anal. Machine
Intell., vol.19, pp.775-780, 1997.
[9] M.Tipping, C.Bishop, "Mixtures of probabilistic principal component
analyzers", Neural Computer, vol.11, pp.443-482, 1999.
[10] S. J. Mckenna, S. Gong, Y. Raja, "Modelling facial colour and identity
with Gaussian mixtures", Pattern Recognition, vol.31, pp.1883-1892,
1998.
[1] M. Turk, A. Pentland, "Face recognition using eigenfaces", in Proc.
IEEE Conf. Computer Vision and Pattern Recognition, 1991,
pp.586-591.
[2] C. Liu, H. Wechsler, "Gabor feature based classification using the
enhanced fisher linear Discriminant model for face recognition", IEEE
Trans. Image Processing, vol.11, pp.467-476, 2002.
[3] J.-W. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Face Recognition
using kernel discriminant analysis algorithms", IEEE Trans. on Neural
Networks, vol.14, pp.117-126, 2003.
[4] H.-C. Kim, D.-J. Kim, Y.-B. Sung, S.-Y. Lee, "Face recognition using
the second-order mixture-of-eigenfaces method", Pattern Recognition,
vol.37, pp.337-349, 2004.
[5] Y. Xu, J. Y. Yang, J.-F. Lu, D.-J. Yu, "An efficient renovation on kernel
fisher discriminant analysis and face recognition experiments", Pattern
Recognition, vol.37, pp.2091-2094, 2004.
[6] A. Samal, P. A. Lyengar, "Automatic recognition and analysis of human
faces and facial expressions: a survey", Pattern Recognition, vol.25,
pp.65-77, 1992.
[7] B. Moghaddam, "Bayesian face recognition", Pattern Recognition,
vol.13, pp.1771-1782, 2000.
[8] L. Wiskott, J.M.Fellous, N.Kruger, C.Malsburg, "Face recognition by
elastic bunch graph matching", IEEE Trans. Pattern Anal. Machine
Intell., vol.19, pp.775-780, 1997.
[9] M.Tipping, C.Bishop, "Mixtures of probabilistic principal component
analyzers", Neural Computer, vol.11, pp.443-482, 1999.
[10] S. J. Mckenna, S. Gong, Y. Raja, "Modelling facial colour and identity
with Gaussian mixtures", Pattern Recognition, vol.31, pp.1883-1892,
1998.
@article{"International Journal of Information, Control and Computer Sciences:64020", author = "Yan Ma and ShunBao Li", title = "The Modified Eigenface Method using Two Thresholds", abstract = "A new approach is adopted in this paper based
on Turk and Pentland-s eigenface method. It was found that the
probability density function of the distance between the projection
vector of the input face image and the average projection vector of
the subject in the face database, follows Rayleigh distribution. In
order to decrease the false acceptance rate and increase the
recognition rate, the input face image has been recognized using two
thresholds including the acceptance threshold and the rejection
threshold. We also find out that the value of two thresholds will be
close to each other as number of trials increases. During the training,
in order to reduce the number of trials, the projection vectors for each
subject has been averaged. The recognition experiments using the
proposed algorithm show that the recognition rate achieves to
92.875% whilst the average number of judgment is only 2.56 times.", keywords = "Eigenface, Face Recognition, Threshold, Rayleigh
Distribution, Feature Extraction", volume = "2", number = "9", pages = "3239-4", }