Abstract
Subspace methods have been successfully applied to face recognition tasks. In this study we propose a face recognition algorithm based on a linear subspace projection. The subspace is found via utilizing a variant of the neighbourhood component analysis (NCA) algorithm which is a supervised dimensionality reduction method that has been recently introduced. Unlike previously suggested supervised subspace methods, the algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. In addition to its feature extraction capabilities, the algorithm also finds the optimal distance-metric that should be used for face-image retrieval in the transformed space. The proposed face-recognition technique significantly outperforms traditional subspace-based approaches particulary in very low-dimensional representations. The method performance is demonstrated across a range of standard face databases.
Highlights
In recent years, automatic face recognition has become one of the most active research fields in computer vision and a large number of different recognition algorithms have been developed
The eigenfaces method which is based on principal component analysis (PCA) [8] and the Fisherfaces method based on the Fisher linear discriminant analysis (LDA) [9] have been applied to face recognition with impressive results
We show experimentally that the neighbourhood component analysis (NCA) approach yields a significant improvement in face-recognition tasks compared to currently used subspace methods
Summary
Automatic face recognition has become one of the most active research fields in computer vision and a large number of different recognition algorithms have been developed. The basic methodology is to (implicitly) apply a nonlinear mapping on the input images and apply linear methods on the resulting feature space Kernel methods such as SVM achieve state-of-the-art results, in the case of kernel-PCA and kernel-LDA the performance improvement in face recognition tasks over linear methods was not found to be significant. We apply a recently proposed linear subspace method, the neighbourhood component analysis (NCA) [12], to the task of face recognition. We show experimentally that the NCA approach yields a significant improvement in face-recognition tasks compared to currently used subspace methods. There is yet another major advantage to the linear subspace method presented here. Comparative face-recognition experiments on several standard face databases are presented in Sections 3 and 4 contains concluding remarks
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