Abstract
In this paper, the problem of frontal view recognition on still images is confronted, using subspace learning methods. The aim is to acquire the frontal images of a person in order to achieve better results in later face or facial expression recognition. For this purpose, we utilize a relatively new subspace learning technique, Clustering based Discriminant Analysis (CDA) against two well-known in the literature subspace learning techniques for dimensionality reduction, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). We also concisely describe spectral clustering which is proposed in this work as a preprocessing step to the CDA algorithm. As classifiers, we use the K-Nearest Neighbor the Nearest Centroid and the novel Nearest Cluster Centroid classifiers. Experiments conducted on the XM2VTS database, demonstrate that PCA+CDA outperforms PCA, LDA and PCA+LDA in Cross Validation inside the database. Finally the behavior of these algorithms, when the size of training set decreases, is explored to demonstrate their robustness.
This work has been funded by the Collaborative European Project MOBISERV FP7-248434 (http://www.mobiserv.eu), An Integrated Intelligent Home Environment for the Provision of Health, Nutrition and Mobility Services to the Elderly.
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References
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 607–626 (2009)
Azran, A., Ghahramani, Z.: Spectral methods for automatic multiscale data clustering. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1(1), 190–197 (2006)
von Luxburg, U.: A tutorial on spectral clustering 17(4), 395–416 (2007)
Jolliffe, I.: Principal Component Analysis. Springer, Heidelberg (1986)
Belhumeur, P.N., Kriegman, J.P.H., Kriegman, D.J.: Eigenfaces vs fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
wen Chen, X., Huang, T.: Facial expression recognition: a clustering-based approach. Pattern Recognition Letters 24, 1295–1302 (2003)
Messer, K., Matas, J., Kittler, J., Luttin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. In: Second International Conference on Audio and Video-based Biometric Person Authentication (AVBPA), pp. 72–77 (1999)
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Maronidis, A., Tefas, A., Pitas, I. (2010). Frontal View Recognition Using Spectral Clustering and Subspace Learning Methods. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_62
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DOI: https://doi.org/10.1007/978-3-642-15819-3_62
Publisher Name: Springer, Berlin, Heidelberg
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