Sparse representations for classification (src) are considered a relevant advance to the biometrics field, but are particularly sensitive to data misalignments. In previous studies, such misalignments were compensated...
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Sparse representations for classification (src) are considered a relevant advance to the biometrics field, but are particularly sensitive to data misalignments. In previous studies, such misalignments were compensated for by finding appropriate geometric transforms between the elements in the dictionary and the query image, which is costly in terms of computational burden. This study describes an algorithm that compensates for data misalignments in src in an implicit way, that is, without finding/applying any geometric transform at every recognition attempt. The authors' study is based on three concepts: (i) sparse representations;(ii) projections on orthogonal subspaces;and (iii) discriminant locality preserving with maximum margin projections. When compared with the classical src algorithm, apart from providing slightly better performance, the proposed method is much more robust against global/local data misalignments. In addition, it attains performance close to the state-of-the-art algorithms at a much lower computational cost, offering a potential solution for real-time scenarios and large-scale applications.
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