Abstract
We present a two-stage dimensional reduction approach to low-dimensional representation. When facial feature data need to be stored in low capacity storing devices, low-dimensional representation of facial images is very important. Our approach is composed of two consecutive mappings of the input data. The first mapping is concerned with best separation of the input data into classes and the second focuses on the mapping that the distance relationship between data points before and after the map is kept as closely as possible. We claim that if data is well-clustered into classes, features extracted from a topology-preserving map of the data are appropriate for recognition when low-dimensional features are to be used. We have presented two novel methods: FLD (Fisher’s Linear Discriminant) combined with SOFM (Self-Organizing Feature Map) method and FLD combined with MDS (Multi-Dimensional Scaling) method. Experimental results using Yale, AT&T and FERET facial image databases show that the recognition performance of our methods degrades gracefully when low-dimensional features are used.
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References
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)
Bartlett, M.S., Martin, H., Sejnowski, T.J.: Independent Component Representations for Face Recognition. In: Proceedings of the SPIE, vol. 3299, pp. 528–539 (1998)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Friedman, J.K., Tukey, J.W.: A Projection Pursuit Algorithm for Exploratoty Data Analysis. IEEE Trans on computers 23, 881–889 (1974)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Inc, Chichester (2001)
Bishop, C.M., Svensén, M.: GTM: The Generative Topographic Mapping. Neural Computation 10(1), 215–234 (1998)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.R.: Fisher Discriminant Analysis with Kernels. In: IEEE Neural Networks for Signal Processing IX, pp. 41–48 (1999)
Carreira-Perpoñán, M.: A Reivew of Dimension Reduction Techniques. Technical Report CS-96-09, Dept. of Computer Science University of Sheffield (1997)
Pcekalska, E., Paclík, P., Duin, R.P.W.: A Generalized Kernel Approach to Dissimilarity-based Classification. Journal of Machine Learning Research 2, 175–211 (2001)
Chandrasiri, N.P., Park, M.C., Naemura, T., Harashima, H.: Personal Facial Expression Space based on Multidimensional Scaling for the Recognition Improvement. In: Proc. IEEE ISSPA 1999, pp. 943–946 (1999)
Choi, J.M., Yi, J.H.: Low-Dimensional Image Representation for Face Recognition. In: Workshop on Multimodal User Authentication (2003)
Phillips, P.J., Moon, H.J., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. on PAMI 22(10), 1090–1104 (2000)
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Choi, J., Yi, J. (2004). A Two-Stage Dimensional Reduction Approach to Low-Dimensional Representation of Facial Images. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_19
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DOI: https://doi.org/10.1007/978-3-540-25948-0_19
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