- July 2017
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- This paper proposes a novel approach for face recognition when only one sample per individual is available. The proposed technique, referred to as MODPL, determines a one-dimensional projection line for each individual in the dataset. Each of these lines discriminates the corresponding individual with respect to the other people in the database. The vector consisting on the projections of the individual's raw data on the different projections lines provides an excellent characterization of the individual. Results obtained using the XM2VTS database show that the proposed technique is capable of achieving classification rates similar to the ones obtained by means of the Uniform-pursuit algorithm and at least 5% higher than other currently used techniques that deal with the one sample problem. Two additional sets of experiments were conducted on the BioID and AR databases, where the proposed algorithm showed a performance similar to the state-of-the-art algorithms. Moreover, the proposed technique allows the visualization of the most discriminative features of the individuals.
- face recognition; one-sample problem; linear regression; single training image; pca; flda