Gabor Feature-Based Fast Neighborhood Component Analysis for Face Recognition
Subspace methods have been very successful in face recognition. Neighborhood components analysis (NCA), one popular subspace method, however, cannot outperform discriminative common vectors (DCV) when applied to face recognition. In this paper, we proposed a Gabor feature-based fast NCA method (Gabor-FNCA). First, we extract multi-scale and multi-orientation Gabor features for more robust and enhanced face recognition. Then, we claimed that the FNCA learning problem would be ill-posed for high dimensional data dimensionality reduction. To address this problem, we first use principal component analysis (PCA) to transform the data in a low-dimensional subspace, and then use the FNCA model which including a Frobenius norm regularizer to learn the linear projection matrix. Experimental results on the ORL and FERET face datasets shows that the proposed Gabor-FNCA method is effective for face recognition.
KeywordsFace recognition Subspace method Neighborhood component analysis Discriminative common vectors Metric learning
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- 3.Shakhnarovich, G., Moghaddam, B.: Face Recognition in Subspaces. In: Li, S.Z., Jain, A.K. (eds.) Hand-book of Face Recognition. Springer (2004)Google Scholar
- 10.Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. Advances in Neural Information Processing Systems 18, 1473–1480 (2006)Google Scholar
- 11.Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. Advances in Neural Information Processing Systems 17, 513–520 (2005)Google Scholar
- 12.Torresani, L., Lee, K.-C.: Large margin component analysis. Advances in Neural Information Processing Systems 19, 1385–1392 (2007)Google Scholar