Abstract
This paper presents a novel pairwise classification framework for face recognition (FR). In the framework, a two-class (intra- and inter-personal) classification problem is considered and features are extracted using pairs of images. This approach makes it possible to incorporate prior knowledge through the selection of training image pairs and facilitates the application of the framework to tackle application areas such as facial aging. The non-linear empirical kernel map is used to reduce the dimensionality and the imbalance in the training sample set tackled by a novel training strategy. Experiments have been conducted using the FERET face database.format.
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References
Wiskott, L., Fellous, J.-M., Küiger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19(7), 775–779 (1997)
Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. IP 11(4), 467–476 (2002)
Yang, P., Shan, S., Gao, W., Li, Z., Zhang, D.: Face Recognition Using Ada-Boosted Gabor Features. In: Proc. IEEE Intl. Conf. Auto. Face and Gesture Recognition, pp. 356–361 (2004)
Patterson, E., Sethuram, A., Albert, M., Ricanek, K., King, M.: Aspects of Age Variation in Facial Morphology Affecting Biometrics. In: Proc. BTAS, pp. 1–6 (2007)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. PAMI 27(1), 4–13 (2005)
Moghaddam, B., Wahid, W., Pentland, A.: Beyond eigenfaces: probabilistic matching for face recognition. In: Proc. IEEE Intl. Conf. Auto. Face and Gesture Recognition, pp. 30–35 (1998)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge (2001)
Phillips, P.J., Moon, H., Rauss, P.J., Rizvi, S.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. PAMI 22(10), 1090–1104 (2000)
Zhou, Z., Chindaro, S., Deravi, F.: Non-Linear Fusion of Local Matching Scores for Face Verification. In: Proc. IEEE Intl. Conf. Auto. Face and Gesture Recognition (2008)
Beveridge, J.R., Bolme, D.S., Draper, B.A., Teixeira, M.: The CSU face identification evaluation system: its purpose, features and structure. Machine Vision and Applications 16(2), 128–138 (2005)
Zhang, B., Shan, S., Chen, X., Gao, W.: Histogram of Gabor phase patterns (HGPP): A novel object representation approach for face recognition. IEEE Trans. IP 16(1), 57–68 (2007)
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Zhou, Z., Chindaro, S., Deravi, F. (2010). Face Recognition Using Balanced Pairwise Classifier Training. In: Weerasinghe, D. (eds) Information Security and Digital Forensics. ISDF 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11530-1_5
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DOI: https://doi.org/10.1007/978-3-642-11530-1_5
Publisher Name: Springer, Berlin, Heidelberg
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