Face Recognition Using Morphological Shared-weight Neural Networks

We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network-s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.

Authors:

• Hossein Sahoolizadeh
• Mahdi Rahimi
• Hamid Dehghani

Keywords:

• Face recognition
• Neural Networks
• Multi-layer Perceptron
• masking.

References:

[1] Y. Won et. al., Morphological Shared-Weight Networks with
Applications to Automatic Target Recognition, Electronics and
Telecommunications Research Institute, Daejon, South Korea, 1995.
[2] Y. Won and P. Gader, Morphological Shared-Weight Neural Network
for Pattern Classification and Automatic Target Detection, University of
Missouri-Columbia, 1995.
[3] D. Haun, K. Hummel, and M. Skubic, Morphological Neural Network
Vision Processing for Mobile Robots, University of Missouri-Columbia,
1997.
[4] U. Uludag and A. Jain, Biometrics, International Conference on Pattern
Recognition, Department of Computer Science and Engineering,
Michigan State University, 1999.
[5] G. Scott and M. Skubic, Face Recognition Using Morphological Shared-
Weight Neural Networks.
[6] V. Starovoitov, D. Samal, and D. Briliuk, Three Approaches for Face
Recognition, The 6-th International Conference on Pattern Recognition
and Image Analysis, Velikiy Novgorod, Russia, 2002.
[7] L. Aryananda, Online and Unsupervised Face Recognition for
Humanoid Robot: Toward Relationship with People, A. I. Lab, MIT,
2001.
[8] K. Jung, Face Recognition Using Kernel Principal Component Analysis,
Michigan State University, 2001.
[9] J. Huang, X. Shao, and H. Wechsler, Face Pose Discrimination Using
Support Vector Machines, George Mason University and University of
Minnesota, 1998.
[10] K. Yiu, M. Mak, and C. Li, Gaussian Mixture Models and Probabilistic
Decision-Based Neural Networks for Pattern Classification: A
Comparative Study, Hong Kong Polytechnic University, HK, China,
1999.
[11] T. Sim, R. Sukthankar, M. Mullin, and S. Baluja, Memory-based Face
Recognition for Visitor identification, The Robotics Institute, Carnegie
Mellon University, 2000.