Abstract: To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called ‘reconstruction proportion’ is exploited to reflect the discriminant information. A novel space called the ‘reconstruction space’ is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.

Key words: Reconstruction proportion, Reconstruction space, Real-time classification, Face recognition

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