Face Recognition using a Kernelization of Graph Embedding

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.

Authors:

• Pang Ying Han
• Hiew Fu San
• Ooi Shih Yin

Keywords:

• Face recognition
• Fisher discriminant
• graph embedding
• kernelization.

References:

[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.