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Face Recognition using a Kernelization of Graph Embedding

Authors: Pang Ying Han, Hiew Fu San, Ooi Shih Yin

Abstract:

Linearization of graph embedding has been emerged as an effective dimensionality reduction technique in pattern recognition. However, it may not be optimal for nonlinearly distributed real world data, such as face, due to its linear nature. So, a kernelization of graph embedding is proposed as a dimensionality reduction technique in face recognition. In order to further boost the recognition capability of the proposed technique, the Fisher-s criterion is opted in the objective function for better data discrimination. The proposed technique is able to characterize the underlying intra-class structure as well as the inter-class separability. Experimental results on FRGC database validate the effectiveness of the proposed technique as a feature descriptor.

Keywords: Face recognition, Fisher discriminant, graph embedding, kernelization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080864

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[1] M. Belkin, P. Niyogi, P, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Proc. of the Conference on Advances in Neural Information Processing System 15, pp. 585-591, 2001.
[2] S.T. Roweis, L. Saul, "Nonlinear dimensionality reduction by Locally Linear Embedding," Science , vol. 290, no.5500, pp. 2323-2326, 2000.
[3] J. Tenenbaum, V. Silva, J. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no.5500, pp. 2319-2323, 2000.
[4] X. He, S. Yan, Y. Hu, P. Niyogi, H. Zhang, "Face recognition using laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, 2005.
[5] X. He, Deng Cai, S. Yan, H.J. Zhang, "Neighborhood Preserving Embedding," in Proc. of the Tenth IEEE International Conference on Computer Vision, pp. 1208-1213, 2005.
[6] S. Yan, D. Xu, B. Zhang, H.J. Zhang, Q. Yang, S. Lin, S, "Graph embedding and extensions: a general framework for dimensionality reduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, 2007.
[7] D. Cai, X. He, K. Zhou, J. Han, H. Bao, "Locality sensitive discriminant analysis," in Proc. of IJCAI, pp. 708-713, 2007.
[8] Y.H. Pang, B.J. Andrew Teoh, Fazly Salleh Abas, "Neighbourhood Preserving Discriminant Embedding in face recognition, Elsevier Journal of Visual Communication and Image Representation, vol. 20, no. 8, pp. 532-542, 2009.
[9] M.H. Yang, "Kernel Eigenfaces vs. Kernel Fisherfaces: face recognition using kernel methods," in Proc. of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 215-220, 2002.
[10] M. Turk, A. Pentland, "Eigenfaces for recognition," J. Cognitive Neuroscience , vol. 3, no. 1, pp. 71-86, 1991.
[11] P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 711-720, 1997.
[12] P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, W. Worek, "Overview of the face recognition grand challenge," in Proc. The IEEE International Conference on Computer Vision and Pattern Recognition, CVPR05, pp. 947-954, 2005.