Low Resolution Face Recognition Using Mixture of Experts
Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.
[1] A. Iranzad, S. Masoudnia, F. Cheraghchi, A. Nowzari-Dalini, R.
Ebrahimpour, in Proceedings of International Conference on Soft Computing
and Pattern Recognition, IEEE Press, Paris, France, pp. 309-313,
2010.
[2] R. Abiantun, M. Savvides, and B. V. K. Vijaya Kumar, How low can
you go? low resolution face recognition study using kernel correlation
feature analysis on the FRGCv2 dataset, in Special Session on Research
at the Biometric Consortium Conference, IEEE Press, New York, NY,
USA, pp. 1-6, 2006
[3] S. Baker and T. Kanade, Hallucinating faces, in Proceedings of 14th
IEEE Conference on Automatic Face and Gesture Recognition, IEEE
Press, Los Alamitos, CA, USA, pp. 83-88, 2000.
[4] H. Chang, D. Yeung, and Y. Xiong, Super-resolution through neighbor
embedding, in Proceedings of IEEE Conference on Computer Vision and
Pattern Recognition, IEEE Press, Los Alamitos, CA, USA, pp. 275-282,
2004.
[5] C. Conde, A. Ruiz, and E. Cabello, PCA vs low resolution images in
face verification, in Proceedings of 12th International Conference on
Image Analysis and Processing, IEEE Press, Los Alamitos, CA, USA,
pp. 63-67, 2003.
[6] R. Ebrahimpour, E. Kabir, and M.R Yousefi, Teacher-directed learning
in view-independent face recognition with mixture of experts using
overlapping eigenspaces, Computer Vision and Image Understanding,
111, 2008, 195-206.
[7] W. Freeman, E. Pasztor, and O. Carmichael, Learning low-level vision,
International Journal of Computer Vision, 40, 2000, 25-47.
[8] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice
Hall, Ontario, Canada, 1999.
[9] P.H. Hennings-Yeomans, S. Baker, and B.V.K. Vijaya Kumar, Recognition
of low-resolution faces using multiple still images and multiple
cameras, in Proceeding of 2th IEEE Conference on Biometrics: Theory,
Applications and Systems, IEEE Press, New York, NY, USA, pp. 1-6,
2008.
[10] A.N. Htwe, Image Interpolation framework using non-adaptive approach
and NL means, International Journal of Network and Mobile Technologies,
1, 2010, 28-32.
[11] I.T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York,
NY, USA, 2002.
[12] B. Li, H. Chang, S. Shan, and X. Chen, Low-resolution face recognition
via coupled locality preserving mappings, IEEE Signal Processing
Letters, 17, 2010, 20-23.
[13] B. Li, H. Chang, S. Shan, X. Chen, and W. Gao, Hallucinating facial
images and features, in Proceedings of 19th International Conference on
Pattern Recognition, IEEE Press, New York, NY, USA, pp. 1-4, 2008.
[14] C. Liu, H. Shum, and C. Zhang, A two-step approach to hallucinating
faces: Global parametric model and local nonparametric model, in
Proceedings of IEEE Conference on Computer Vision and Pattern
Recognition, IEEE Press, Los Alamitos, CA, USA, pp. 192-198, 2001.
[1] A. Iranzad, S. Masoudnia, F. Cheraghchi, A. Nowzari-Dalini, R.
Ebrahimpour, in Proceedings of International Conference on Soft Computing
and Pattern Recognition, IEEE Press, Paris, France, pp. 309-313,
2010.
[2] R. Abiantun, M. Savvides, and B. V. K. Vijaya Kumar, How low can
you go? low resolution face recognition study using kernel correlation
feature analysis on the FRGCv2 dataset, in Special Session on Research
at the Biometric Consortium Conference, IEEE Press, New York, NY,
USA, pp. 1-6, 2006
[3] S. Baker and T. Kanade, Hallucinating faces, in Proceedings of 14th
IEEE Conference on Automatic Face and Gesture Recognition, IEEE
Press, Los Alamitos, CA, USA, pp. 83-88, 2000.
[4] H. Chang, D. Yeung, and Y. Xiong, Super-resolution through neighbor
embedding, in Proceedings of IEEE Conference on Computer Vision and
Pattern Recognition, IEEE Press, Los Alamitos, CA, USA, pp. 275-282,
2004.
[5] C. Conde, A. Ruiz, and E. Cabello, PCA vs low resolution images in
face verification, in Proceedings of 12th International Conference on
Image Analysis and Processing, IEEE Press, Los Alamitos, CA, USA,
pp. 63-67, 2003.
[6] R. Ebrahimpour, E. Kabir, and M.R Yousefi, Teacher-directed learning
in view-independent face recognition with mixture of experts using
overlapping eigenspaces, Computer Vision and Image Understanding,
111, 2008, 195-206.
[7] W. Freeman, E. Pasztor, and O. Carmichael, Learning low-level vision,
International Journal of Computer Vision, 40, 2000, 25-47.
[8] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice
Hall, Ontario, Canada, 1999.
[9] P.H. Hennings-Yeomans, S. Baker, and B.V.K. Vijaya Kumar, Recognition
of low-resolution faces using multiple still images and multiple
cameras, in Proceeding of 2th IEEE Conference on Biometrics: Theory,
Applications and Systems, IEEE Press, New York, NY, USA, pp. 1-6,
2008.
[10] A.N. Htwe, Image Interpolation framework using non-adaptive approach
and NL means, International Journal of Network and Mobile Technologies,
1, 2010, 28-32.
[11] I.T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York,
NY, USA, 2002.
[12] B. Li, H. Chang, S. Shan, and X. Chen, Low-resolution face recognition
via coupled locality preserving mappings, IEEE Signal Processing
Letters, 17, 2010, 20-23.
[13] B. Li, H. Chang, S. Shan, X. Chen, and W. Gao, Hallucinating facial
images and features, in Proceedings of 19th International Conference on
Pattern Recognition, IEEE Press, New York, NY, USA, pp. 1-4, 2008.
[14] C. Liu, H. Shum, and C. Zhang, A two-step approach to hallucinating
faces: Global parametric model and local nonparametric model, in
Proceedings of IEEE Conference on Computer Vision and Pattern
Recognition, IEEE Press, Los Alamitos, CA, USA, pp. 192-198, 2001.
@article{"International Journal of Information, Control and Computer Sciences:56587", author = "Fatemeh Behjati Ardakani and Fatemeh Khademian and Abbas Nowzari Dalini and Reza Ebrahimpour", title = "Low Resolution Face Recognition Using Mixture of Experts", abstract = "Human activity is a major concern in a wide variety of
applications, such as video surveillance, human computer interface
and face image database management. Detecting and recognizing
faces is a crucial step in these applications. Furthermore, major
advancements and initiatives in security applications in the past years
have propelled face recognition technology into the spotlight. The
performance of existing face recognition systems declines significantly
if the resolution of the face image falls below a certain level.
This is especially critical in surveillance imagery where often, due to
many reasons, only low-resolution video of faces is available. If these
low-resolution images are passed to a face recognition system, the
performance is usually unacceptable. Hence, resolution plays a key
role in face recognition systems. In this paper we introduce a new
low resolution face recognition system based on mixture of expert
neural networks. In order to produce the low resolution input images
we down-sampled the 48 × 48 ORL images to 12 × 12 ones using
the nearest neighbor interpolation method and after that applying
the bicubic interpolation method yields enhanced images which is
given to the Principal Component Analysis feature extractor system.
Comparison with some of the most related methods indicates that
the proposed novel model yields excellent recognition rate in low
resolution face recognition that is the recognition rate of 100% for
the training set and 96.5% for the test set.", keywords = "Low resolution face recognition, Multilayered neuralnetwork, Mixture of experts neural network, Principal componentanalysis, Bicubic interpolation, Nearest neighbor interpolation.", volume = "5", number = "8", pages = "897-5", }