Biomedical Engineering Reference
In-Depth Information
We examined the proposed recognition system also in the task of face recogni-
tion. For this purpose we used the ORL database. To obtain statistically reliable
results, we selected the samples for classifier training randomly, and we used the
rest of the samples for testing. We conducted ten experiments with different
numbers of training samples. For five training samples and five test samples of
each face, we obtained 0.1% errors. We also tried other combinations of training
and test samples and obtained good results for each combination (see Table 4.5).
The proposed recognition system was also tested for the micro-object shape
recognition problem. We will discuss this task and the results obtained in the
micromechanics section.
References
1. L.Bottou, C. Cortes, J. Denker, H. Drucker, L. Guyon, L. Jackel, Y. LeCun, U. Muller, E.
Sackinger, P. Simard, V. Vapnik, Comparison of Classifier Methods: A Case Study in Hand-
written Digit Recognition, Proceedings of 12th IAPR Intern. Conf. on Pattern Reco
gnition
,
1994, Vol. 2, pp. 77-82.
3. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based Learning Applied to Document
Recognition,
Proceedings of the IEEE
, Vol. 86, No. 11, 1998, pp. 2278-2344.
4. M.S. Hoque, M.C. Fairhurst, A Moving Window Classifier for Off-line Character Recogni-
tion, Proceedings of the 7th International Workshop on Frontiers in Handwriting Recognition,
2000, pp. 595-600.
5. S. Belongie, J. Malik, J. Puzicha, Matching Shapes, Proceedings of the 8th IEEE International
Conference on Computer Vision ICCV, Vol.1, 2001, pp. 454-461.
6. S. Belongie, J. Malik, J. Puzicha, Shape Matching and Object Recognition Using Shape
Contexts,
IEEE Transactions on Pattern Analysis and Machine Intelligence
, Vol. 24, No. 4,
2002, pp. 509-522.
7. E. Kussul, T. Baidyk, L. Kasatkina, V. Lukovich, Rosenblatt Perceptrons for Handwritten
Digit Recognition. Proceedings of International Joint Conference on Neural Networks
IJCNN'01. Washington, D.C., USA, July 15-19, 2001, pp. 1516-1520.
8. E. Kussul, T. Baidyk, Improved Method of Handwritten Digit Recognition Tested on MNIST
Database, Proceedings of the 15th Intern Conf. on Vision Interface, Calgary, Canada, 2002,
pp. 192-197.
9. Cheng-Lin Liu, K.Nakashima, H.Sako, H. Fujisawa, Handwritten Digit Recognition Using
State-of-the-art Techniques, Proceedings of the 8-th International Workshop on Frontiers in
Handwritten Recognition, Ontario, Canada, August 2002, pp. 320-325.
10. Teewoon Tan, Hong Yan, Object Recognition Using Fractal Neighbor Distance: Eventual
Convergence and Recognition Rates. Proceedings of the 15th IEEE International Conference
on Pattern Recognition, Vol. 2, 3-7 Sept. 2000, pp. 781-784.
11. Meng Joo Er, Shiqian Wu, Juwei Lu, Hock Lye Toh, Face Recognition with Radial Basis
Function (RBF) Neural Networks,
IEEE Transactions on Neural Networks
, Vol. 13, No. 3,
May 2002, pp. 697-710.
12. S. Singh, M. Singh, M. Markou, Feature Selection for Face Recognition Based on Data
Partitioning, Proceedings of the 16th International conference on pattern recognition,
Vol. 1, 2002, pp. 680-683.
13. R. Javad Haddadnia, Majid Ahmadi, Karim Faez, An Efficient Method for Recognition of
Human Faces Using Higher Orders Pseudo Zernike Moment Invariant, Proceedings of the
